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Summarize the top 30 most popular arXiv papers on Reddit, Hacker News and Hugging Face in the last 30 days. Source: Maintained by @ota.bsky.social

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Paper@paper· 28d

[23/30] 231 Likes, 1 Comments, 3 Posts 2607.05391, cs․AI | cs․CL | cs․LG | cs․MA | cs․RO, 07 Jul 2026 🆕LLM-as-a-Verifier: A General-Purpose Verification Framework Jacky Kwok, Shulu Li, Pranav Atreya, Yuejiang Liu, Yixing Jiang, Chelsea Finn, Marco Pavone, Ion Stoica, Azalia Mirhoseini

Scaling pre-training, post-training, and test-time compute have become the central paradigms for improving the capabilities of LLMs.

In this work, we identify verification, the ability to determine the correctness of a solution, as a new scaling axis.

To unlock this and demonstrate its effectiveness, we introduce LLM-as-a-Verifier, a general-purpose verification framework that provides fine-grained feedback for agentic tasks without requiring additional training.

Unlike standard LM judges that prompt LLMs to produce discrete scores for candidate solutions, LLM-as-a-Verifier computes the expectation over the distribution of scoring token logits to generate continuous scores.

This probabilistic formulation enables verification to scale along multiple dimensions: (1) score granularity, (2) repeated evaluation, and (3) criteria decomposition.

In particular, we show that scaling the scoring granularity leads to better separation between positive and negative solutions, resulting in more calibrated comparisons.

Moreover, scaling repeated evaluation and criteria decomposition consistently lead to additional gains in verification accuracy through variance and complexity reduction.

We further introduce a cost-efficient ranking algorithm for selecting the best solution among candidates using the verifier's continuous scores.

LLM-as-a-Verifier achieves state-of-the-art performance on Terminal-Bench V2 (86.5%), SWE-Bench Verified (78.2%), RoboRewardBench (87.4%), and MedAgentBench (73.3%).

Beyond verification, the fine-grained signals from LLM-as-a-Verifier can also serve as a proxy for estimating task progress.

We build an extension for Claude Code, enabling developers to monitor and improve their own agentic systems.

Finally, we show that LLM-as-a-Verifier can provide dense feedback for RL, improving the sample efficiency of SAC and GRPO on robotics and mathematical reasoning benchmarks.

Paper@paper· 28d

[30/30] 210 Likes, 9 Comments, 2 Posts 2606.25041, cs․CV | cs․AI | cs․GR | cs․SD, 29 Jun 2026 🆕Wan-Streamer v0.1: End-to-end Real-time Interactive Foundation Models Lianghua Huang, Zhi-Fan Wu, Wei Wang, Yupeng Shi, Mengyang Feng, Junjie He, Chen-Wei Xie, Yu Liu, Jingren Zhou, Ang Wang, Ban...

Paper@paper· 29d

[17/30] 160 Likes, 9 Comments, 1 Posts 2607.01504, cs․SE | cs․LO | cs․PL, 01 Jul 2026 🆕Kani: A Model Checker for Rust Rémi Delmas, Zyad Hassan, Qinheping Hu, Rahul Kumar, Felipe R. Monteiro, Thanh Nguyen, Adrián Palacios, Celina Val, Michael Tautschnig, Justus Adam, Daniel Schwartz-Narbon...

Rust's ownership type system prevents memory errors in safe code, but certain desirable properties remain orthogonal to compilation: the soundness of unsafe operations (e.g., raw pointer dereferences), functional correctness, and absence of runtime panics.

We present Kani, an open-source model checker for Rust that pushes bounded model checking beyond bug-finding to provide correctness guarantees for these properties.

Kani compiles proof harnesses from Rust's Mid-level Intermediate Representation (MIR) into CBMC's bit-precise verification engine, automatically checking a comprehensive set of safety properties with no user annotation.

To extend verification from bounded to unbounded, Kani provides a specification language comprising function contracts, loop contracts, quantifiers, and function stubbing.

We demonstrate feasibility through case studies on industrial Rust projects, where contracts upgraded verification from panic-freedom to functional correctness, uncovering six previously unknown bugs.

Kani operates at scale in production CI, with over 16,000 harnesses verified per code change in the Rust standard library verification campaign.

Paper@paper· 29d

[20/30] 153 Likes, 29 Comments, 5 Posts 2606.28277, cs․LG | cs․AI | cs․CL | cs․CY, 26 Jun 2026 🆕Towards Automating Scientific Review with Google's Paper Assistant Tool Rajesh Jayaram, Drew Tyler, David Woodruff, Corinna Cortes, Yossi Matias, Vahab Mirrokni, Vincent Cohen-Addad

Artificial intelligence is driving a revolution in scientific discovery, accelerating everything from hypothesis generation to mathematical theorem proving.

However, this rapid acceleration is creating a systemic challenge: traditional human peer review cannot scale to match the influx of AI-assisted science.

Ultimately, to resolve this tension, we must also deploy AI to accelerate the verification and review process itself.

To frame the discussion around this transition, we propose a taxonomy consisting of four progressive levels of AI-human collaboration in scientific evaluation, and discuss various trade-offs involved with each.

As a step toward this future, we introduce the Paper Assistant Tool (PAT), an agentic AI framework built for deep scientific review and verification.

PAT ingests full scientific manuscripts and produces a comprehensive evaluation, checking theoretical results, validating experiments, suggesting improvements, and identifying potential flaws.

By utilizing inference scaling techniques, PAT is able to identify deeper issues than a single model call alone, achieving a 34% improvement over zero-shot recall on mathematical errors in the SPOT benchmark.

Pilot deployments of PAT as a pre-submission tool for authors at two major Computer Science conferences -- STOC and ICML -- demonstrate its ability to identify critical errors and suggest substantive improvements to research papers.

By catching errors early, PAT eases the cognitive burden placed on referees, while preserving their control over the outcomes of the review process.

Paper@paper· 29d

[21/30] 143 Likes, 215 Comments, 3 Posts 2606.19777, physics․soc-ph | econ․GN, 18 Jun 2026 🆕Have Data Centers Raised Your Electric Bill? Causal Evidence from the United States Asa Watten, John Bistline, Geoffrey Blanford

We estimate that data centers caused average retail electricity rates to fall modestly in the United States from 2015 to 2024 using an instrumental variables approach.

Despite prevailing sentiment, the finding is consistent with economic reasoning: existing large power system fixed costs, economies of scale in transmission and distribution, and declining unit costs for generation imply that durable demand growth lowers average prices.

We find patterns of economies of scale for transmission, distribution, and generation costs as well as within and across retail customer classes.

We caution that future supply constraints could reverse the effect.

Paper@paper· 29d

[23/30] 125 Likes, 22 Comments, 1 Posts 2607.07267, cs․CY | cs․CL, 08 Jul 2026 🆕Billions of Sketches Reveal Hidden Cultural Variation in Human Concepts Arianna Pera, Mauro Martino, Nima Dehmamy, Douglas Guilbeault, Luca Maria Aiello, Andrea Baronchelli

Claims about the universality of human concepts have been predominantly assessed through linguistic similarity across languages and cultures.

However, words are effective as communication devices because they compress rich experiential variation into shared conventions, potentially obscuring hidden individual and cultural differences in how concepts are mentally represented.

Here, we analyse 2.6 billion human-made sketches of common concepts from 236 countries and territories to examine conceptual structure through people's visual imagination.

Consistent with recent work on image-based cognition, we find that single concepts unfold into multiple distinct visual exemplars, revealing latent information about similarities and differences in conceptual structure across cultures.

This variation is strongest for concepts involving haptic interaction, suggesting that visual imagery reflects variation in embodied experience as much as conventional definitions.

Comparing embedding models of sketches with word embedding models across languages, we find that their geometries diverge, with visual representations preserving rich semantic and cultural structure that language models compress.

Cross-cultural similarities derived from sketches align 45% more closely with established cultural distances than do text-based measures.

Together, these results suggest that patterns of human conceptual universality may depend critically on the modality through which concepts are measured, with large-scale sketching providing a direct, high-resolution probe of conceptual diversity across embodied and cultural dimensions of thought.

Paper@paper· 29d

[24/30] 108 Likes, 15 Comments, 4 Posts 2606.15991, cs․PL, 14 Jun 2026 🆕Fearless Concurrency on the GPU Melih Elibol, Jared Roesch, Isaac Gelado, Eric Buehler, Michael Garland

Rust has made safe systems programming practical on the CPU, but writing custom GPU kernels in Rust still forces programmers outside the language's ownership guarantees.

We present cuTile Rust, a tile-based system for safe, idiomatic GPU kernel authoring in Rust.

cuTile Rust extends Rust's ownership discipline to tile-based GPU kernels: mutable outputs are split into disjoint pieces, kernel launches preserve the host-side ownership contract, and programmers can opt out locally when they need lower-level control.

The system also provides a composable host execution model spanning synchronous launches, asynchronous pipelines, and CUDA graph replay.

Our evaluation shows that these abstractions can preserve performance on high-end GPUs.

On the NVIDIA B200 GPU, cuTile Rust achieves 7 TB/s for element-wise operations and 2 PFlop/s for GEMM (96% of cuBLAS), matching cuTile Python within measurement noise.

Grout, a cuTile-Rust-based inference engine, exercises cuTile Rust across an end-to-end Qwen3 inference path.

In batch-1 decode, Grout reaches 171 generated tokens/s for Qwen3-4B on the NVIDIA GeForce RTX 5090 and 82 generated tokens/s for Qwen3-32B on the B200, competitive with vLLM and SGLang and consistent with an HBM roofline sanity check.

Paper@paper· 29d

[25/30] 97 Likes, 2 Comments, 2 Posts 2607.00272, cs․RO | cs․AI | cs․MA, 30 Jun 2026 🆕ASPIRE: Agentic /Skills Discovery for Robotics Runyu Lu, Yubo Wu, Ethan Kou, Letian Fu, Wenli Xiao, Ajay Mandlekar, Yinzhen Xu, Guanya Shi, Ken Goldberg, Ang Chen, Mosharaf Chowdhury, Yuke Zhu, Linxi "Jim"...

Traditional robot programming is challenging: it requires orchestrating multimodal perception, managing physical contact dynamics, and handling diverse configurations and execution failures.

We introduce ASPIRE (Agentic Skill Programming through Iterative Robot Exploration), a continual learning system that autonomously writes and refines robot control programs in a code-as-policy paradigm while compounding experience into a reusable skill library.

ASPIRE discovers skills that persist across tasks, simulation and real-world settings, and embodiments.

It operates in an open-ended loop with three components: (1) a closed-loop robot execution engine that exposes fine-grained multimodal traces, enabling autonomous failure diagnosis, repair synthesis, and validation; (2) a continually expanding skill library that distills validated fixes into reusable, transferable knowledge; and (3) evolutionary search that generates diverse task sequences and control programs to explore beyond single-trajectory refinement.

ASPIRE surpasses prior methods by up to 77% on LIBERO-Pro manipulation under perturbation, 72% on Robosuite bimanual handover, and 32% on BEHAVIOR-1K long-horizon household tasks.

Its accumulated library also enables zero-shot generalization to unseen long-horizon tasks: on LIBERO-Pro Long, ASPIRE achieves 31% success versus 4% for prior methods despite their use of test-time reasoning and retries.

Finally, simulation-discovered skills provide initial evidence of sim-to-real transfer, substantially reducing real-robot programming effort across different embodiments and robot APIs.

Paper@paper· 29d

[27/30] 90 Likes, 7 Comments, 2 Posts 2606.17861, cs․CL, 16 Jun 2026 🆕GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine? Tongxu Luo, Rongsheng Wang, Jiaxi Bi, Chenming Xu, Zhengyang Tang, Jianlong Chen, Juhao Liang, Ke Ji, Shuqi Guo, Yuhao Du, Fan Bu, Wenyu Du,...

Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems.

Unlike traditional coding tasks, game generation takes place within a game engine, where scripts, scenes, assets, rendering, and runtime interactions must jointly produce coherent gameplay.

We formalize end-to-end game generation as the problem of producing a complete game artifact that realizes a specification through observable player-game interaction in a target environment.

We argue that evaluating this setting requires three desiderata: Engine Grounding, Artifact Completeness, and Interactive Verification.

We propose an interaction-grounded evaluation framework that assesses executable gameplay through replayed demonstrations and rubric-guided multimodal judging.

We instantiate this framework as GameCraft-Bench, a benchmark comprising 140 Godot tasks across 15 game families.

Evaluations of frontier coding agents show that end-to-end game generation remains highly challenging: the strongest agent achieves only 41.46%, and most agents score below 40%.

Further analysis reveals that while agents often implement recognizable mechanics, they struggle to deliver complete games with sufficient content, functional visual feedback, and coherent presentation.

See https://tongxuluo.github.io/gamecraft-bench-website for demos, code, and data.

Paper@paper· 29d

[28/30] 86 Likes, 16 Comments, 2 Posts 2606.23179, cs․LG | cs․NE | cs․SC | math․NA, 22 Jun 2026 🆕EML Trees Are Universal Approximators Joe Germany, Elie Abdo, Joseph Bakarji

$The recently introduced EML (Exp-Minus-Log) function acts as continuous analogue of NAND gates, providing a compositional building block capable of representing elementary functions. In this work, we study the expressive power of tree-structured compositions of EML functions. We show that such trees enjoy a universal approximation property for functions in $W^{k, \infty}$ for $k \in \mathbb N$, drawing on classical neural network approximation arguments while exploiting the ability to explicitly construct EML trees that mimic polynomial representations. We further propose a learning algorithm for EML-type trees equipped with fitting parameters, and demonstrate its feasibility in practical optimization problems. Our results establish EML trees as a theoretically grounded framework for function approximation.$

Paper@paper· 29d

[29/30] 86 Likes, 7 Comments, 2 Posts 2607.02980, cs․CL | cs․AI, 03 Jul 2026 🆕Hierarchical Sparse Attention Done Right: Toward Infinite Context Modeling Xiang Hu, Xinyu Wei, Hao Gu, Minshen Zhang, Tian Liang, Huayang Li, Lei Zhu, Yan Wang, Sirui Han, Yushi Bai, Kewei Tu, Haitao Mi, Leo Liang

$Scaling modern large language models (LLMs) to long contexts is limited by the quadratic computation cost, and poor length extrapolation of dense attention. Chunk-wise sparse attention offers a promising alternative, but all existing methods fall short of full attention because of their inaccurate chunk selection. We propose Hierarchical Landmark Sparse (HiLS) Attention, a chunk-wise sparse attention mechanism that learns chunk selection end-to-end under the language-modeling (LM) loss. HiLS factorizes attention hierarchically: each query performs attention independently with each retrieved chunk to extract chunk-specific information, and the resulting outputs are fused according to chunk retrieval scores. By incorporating retrieval scores into the forward attention computation, HiLS optimizes them directly with the LM loss, enabling end-to-end retrieval learning and native sparse training. Experimental results show that HiLS-Attention achieves performance comparable to, and in some cases better than, full attention at in-domain context lengths. Meanwhile, HiLS-Attention extrapolates more than $64\times$ the training context length with 90% retrieval accuracy, far beyond full attention. Moreover, existing full-attention models can be converted to HiLS-Attention with lightweight continued pretraining, preserving in-domain performance while acquiring ultra-long-context extrapolation. Together with its sparse KV access and computation, HiLS-Attention breaks the usual efficiency-performance trade-off, enabling long-context LLMs that are both more efficient and more effective on general long-context tasks than their full-attention counterparts.$

Paper@paper· 29d

[30/30] 82 Likes, 37 Comments, 1 Posts 2607.06377, math․HO, 07 Jul 2026 🆕Automation Without Understanding Jun-Yong Park

Two developments are unfolding at once: artificial intelligence systems have begun to produce genuine research-level mathematics, and the United States is weakening the pipeline that produces humans capable of understanding what such systems are doing.

This essay argues that, taken together, these developments amount to a strategic error.

Mathematical capacity, which is the trained ability to verify, interpret, and challenge mathematical reasoning, is not a byproduct of theorem production but a form of infrastructure, built over generations by institutions that cannot be reconstituted on demand.

Drawing on the May 2026 AI disproof of a longstanding Erdős conjecture on the planar unit distance problem and on recent disruptions to federal support for the mathematical sciences, the essay makes the case for treating mathematical capacity as a strategic asset on a par with semiconductor capability.

It further proposes, among other measures, that AI systems performing consequential reasoning be required to expose their decision-critical claims in formal, machine-checkable form, converting part of AI reasoning from opaque persuasion into auditable structure.

Paper@paper· Jul 12

[28/30] 214 Likes, 2 Comments, 4 Posts 2606.17030, cs․CV, 17 Jun 2026 🆕Qwen-RobotWorld Technical Report: Unifying Embodied World Modeling through Language-Conditioned Video Generation Jie Zhang, Xiaoyue Chen, Anzhe Chen, Dayiheng Liu, Deqing Li, Gengze Zhou, Hale Yin, Haoqi Yuan, Haoyang Li, ...

We introduce Qwen-RobotWorld, a language-conditioned video world model for embodied intelligence.

With natural language as a unified action interface, it predicts physically grounded future visual trajectories from current observations across robotic manipulation, autonomous driving, indoor navigation, and human-to-robot transfer.

This unified formulation provides three promising application directions: synthetic data generation for policy training augmentation, scalable virtual environments for policy evaluation, and language-guided planning signals for downstream robot control.

This is achieved through a three-part design:

a) Double-Stream MMDiT with MLLM Action Encoding, where a 60-layer double-stream diffusion transformer couples frozen Qwen2.5-VL semantics with video-VAE latents through layer-wise joint attention;

b) Embodied World Knowledge (EWK), an 8.6M video-text corpus (200M+ frames) with action-language mapping over 20+ embodiments and 500+ action categories; and

c) General+Expert Progressive Curriculum, a two-stage training strategy that first learns general visual priors and then injects embodied specialization under a shared language interface.

Extensive results show strong competitiveness: ranks 1st overall on EWMBench and DreamGen Bench, outperforms all open-source models on WorldModelBench and PBench.

Additional zero-shot analyses on RoboTwin-IF benchmark further support robust generalization and multi-view consistency.

Paper@paper· Jul 12

[29/30] 212 Likes, 8 Comments, 5 Posts 2607.02770, cs․CL | cs․AI, 02 Jul 2026 🆕Gemma 4 Technical Report Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres, Alek Andreev, Olivier Bachem, Ian Ballantyne, Cormac Brick, Victor Cărbune, Michelle Casbon, Mayank Chaturvedi, Victor Cotruta,...

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family.

Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parameters.

Alongside improved vision and audio encoders for all model sizes, we propose a unified, encoder-free architecture for our 12B model, which ingests raw audio and image patches.

Furthermore, we integrate a thinking mode, enabling Gemma models to generate reasoning traces prior to responding.

We improve inference speed, memory, and compute efficiency, as well as long-context abilities through critical design choices.

Gemma 4 establishes a leap in performance across STEM, multimodal, and long-context benchmarks, and rivals larger, frontier open models in human-rated tasks.

Paper@paper· Jul 12

[30/30] 207 Likes, 3 Comments, 2 Posts 2606.20515, cs․CV, 28 Jun 2026 🆕S-Agent: Spatial Tool-Use Elicits Reasoning for Spatial Intelligence Yalun Dai, Hao Li, Shulin Tian, Runmao Yao, Yuhao Dong, Fangzhou Hong, Zhaoxi Chen, Fangfu Liu, Baoliang Tian, Dingwen Zhang, Tao Wang, Kim-Hui Yap, Ziwe...

$Real-world spatial intelligence requires reasoning over a continuous and evolving 3D world, yet existing VLMs and tool-augmented agents largely remain tied to static, stateless inference from isolated visual observations. We introduce \textbf{\textsc{S-Agent}}, a spatial tool-use agentic paradigm for understanding and reasoning over continuous multi-view images and videos. By formulating spatial reasoning as spatio-temporal evidence accumulation rather than isolated frame-level prediction, \textsc{S-Agent} reshapes spatial perception into scene-centric understanding beyond frame-centric recognition. Specifically, \textsc{S-Agent} casts the VLM as a semantic planner that decides what evidence is needed, while a hierarchy of spatial tools and experts grounds objects in 2D, lifts them into 3D geometric evidence, and aggregates this evidence into high-level spatial knowledge (\textit{e.g.}, counting, measurement, orientation, and relative position). Additionally, a temporal memory mechanism, including Scene Memory for maintaining the evolving scene state and Agent Memory for accumulating reasoning context, enables evidence integration across frames and reasoning steps. Comprehensive experiments on multi-view and video spatial reasoning benchmarks show that \textsc{S-Agent} consistently improves both open-source and closed-source VLMs in a training-free manner. Beyond inference-time augmentation, supervised fine-tuning (SFT) on \textsc{S-Agent}-generated spatial trajectories \textsc{S-300K} yields \textsc{S-Agent-8B}, a compact spatial agent that significantly surpasses similar-scale baselines (e.g., Qwen3-VL-8B) and performs comparably to advanced closed-source models (e.g., GPT-5.4 and Gemini 3).$

Paper@paper· Jul 11

[10/30] 414 Likes, 2 Comments, 2 Posts 2607.05147, cs․AI | cs․CL, 06 Jul 2026 🆕DSpark: Confidence-Scheduled Speculative Decoding with Semi-Autoregressive Generation Xin Cheng, Xingkai Yu, Chenze Shao, Jiashi Li, Yunfan Xiong, Yi Qian, Jiaqi Zhu, Shirong Ma, Xiaokang Zhang, Jiasheng Ye, Qinyu...

Speculative decoding accelerates Large Language Model (LLM) inference by decoupling draft generation from target verification.

While recent parallel drafters efficiently propose long token sequences in a single forward pass, they suffer from rapid acceptance decay due to a lack of inter-token dependencies.

Furthermore, indiscriminately verifying these extended blocks wastes critical batch capacity on tokens with high rejection risks, severely degrading throughput in high-concurrency serving systems.

We introduce DSpark, a speculative decoding framework that unifies high-throughput parallel generation with adaptive, load-aware verification.

To maintain draft quality, DSpark utilizes a semi-autoregressive architecture, coupling a parallel backbone with a lightweight sequential module, to introduce intra-block dependency modeling and mitigate suffix decay.

To optimize system efficiency, DSpark employs confidence-scheduled verification, dynamically tailoring the verification length for each request based on estimated prefix survival probabilities and engine-specific throughput profiles.

On offline benchmarks across diverse domains, DSpark substantially improves the accepted length over state-of-the-art autoregressive and parallel drafters.

When deployed within the DeepSeek-V4 serving system under live user traffic, DSpark successfully mitigates verification waste.

Compared to the established production baseline (MTP-1), DSpark accelerates per-user generation speeds by 60 to 85 percent at matched throughput levels.

More importantly, by preventing severe throughput degradation under strict interactivity constraints, it enables performance tiers that were previously unattainable, shifting the Pareto frontier of our serving system.

Paper@paper· Jul 11

[30/30] 219 Likes, 2 Comments, 2 Posts 2606.30626, cs․AI, 29 Jun 2026 🆕DOPD: Dual On-policy Distillation Xinlei Yu, Gen Li, Qingyi Si, Guibin Zhang, Yuqi Xu, Congcong Wang, Shuai Dong, Kaiwen Tuo, Xiangyu Zeng, Kaituo Feng, Qunzhong Wang, Yang Shi, Xiaobin Hu, Xiangyu Yue, Jiaqi Wang, Shuiche...

On-policy distillation (OPD) offers superior capacity transfer by supervising student-sampled trajectories with dense token-level signals.

To furnish high-quality supervision sources and thereby elevate the performance frontier of distillation, an intuitive direction is to infuse privileged information to either teacher or student itself.

However, this additional input induces a potential failure mode we dub privilege illusion: a pattern that conflates the transferable capability gap that students are meant to close, and the information asymmetry gap that can only be mimicked but never replicated.

This issue is further amplified by the inherent non-uniformity of token-level supervision, where only a small subset of tokens carries pivotal capability-bearing signals.

To this end, we propose DOPD, an advantage-aware dual distillation paradigm that dynamically routes token-level supervision between privileged teacher and privileged student policies based on their advantage gap and relative probabilities.

Each token receives supervision of different strength, objective, and strategy from either teacher or student itself, which transfers credible capability while simultaneously receiving auxiliary signals, to alleviate privilege illusion.

Extensive experiments on both large language model (LLM) and vision-language model (VLM) settings demonstrate that DOPD consistently outperforms Vanilla OPD and other counterparts.

Further results on stability, robustness, continual learning, and out-of-distribution tasks validate its superiority.

Paper@paper· Jul 9

[1/30] 1890 Likes, 102 Comments, 2 Posts 2606.13350, astro-ph․EP, 11 Jun 2026 🆕An unidentified absorption feature at 5.11 $μ$m on the surface of Titan and Pluto from JWST spectroscopy B. Bézard, E. Lellouch, M. Camarca, J. I. Lunine, E. Quirico, C. A. Nixon, N. A. Teanby, P. Rannou, S. Rodr...

$Titan possesses a thick N$_2$-CH$_4$ atmosphere that makes it difficult to study its surface spectroscopically. The chemical composition of the solid surface of Titan thus remains very uncertain. By leveraging JWST's high sensitivity and large spectral coverage, we searched for any signature from Titan's surface in the broad and less explored 5-$μ$m atmospheric window. We also investigated the JWST spectrum of Pluto which has a thin Titan-like atmosphere. We made selections of JWST NIRSpec and MIRI spectra around Titan's disk center and compared the NIRSpec average spectrum with a radiative transfer model including gas and haze opacity. We detected an unidentified absorption in both NIRSpec and MIRI spectra of Titan centered at 5.113 $μ$m (1956 cm$^{-1}$) and 6-7% deep. The width of the feature is 0.024$\pm$0.0008 $μ$m (9.2$\pm$0.3 cm$^{-1}$) in the NIRSpec spectrum recorded on the trailing side and is possibly 25% narrower in the MIRI spectrum of the leading side. This absorption most likely originates from the surface. We could not identify this signature among published laboratory spectra of ices relevant to Titan's atmospheric compounds but present a few plausible candidates. A 4-5% deep absorption is also present in the MIRI spectrum of Pluto but is about 3 times broader than on Titan's trailing side.$

Paper@paper· Jul 9

[15/30] 315 Likes, 60 Comments, 1 Posts 2607.04371, cs․AI, 07 Jul 2026 🆕Nemotron-Labs-3-Puzzle-75B-A9B: Compressing Hybrid MoE LLMs Akhiad Bercovich, Talor Abramovich, Daniel Afrimi, Shay Aharon, Nir Ailon, Vladimir Anisimov, Omer Ullman Argov, Maor Ashkenazi, Tomer Asida, Nave Assaf, Tomer B...

We present Nemotron-Labs-3-Puzzle-75B-A9B, a compressed variant of Nemotron-3-Super optimized for interactive deployment.

We designed the model to maximize server throughput under high user throughput constraints.

In interactive serving workloads on a single 8xB200 node, Puzzle-75B-A9B achieves approximately 2x higher server throughput than Nemotron-3-Super at matched user throughput constraints.

In ultra-long-context deployment on a single H100 GPU, the compressed model increases 1M-token concurrency from 1 request to 8 requests.

Puzzle-75B-A9B is constructed using a multi-stage pipeline that combines the Iterative Puzzle compression framework with knowledge distillation, reinforcement learning, quantization, and a Multi-Token Prediction head.

The compression process jointly optimizes heterogeneous MoE pruning, active parameter budget, and Mamba pruning to improve inference efficiency while preserving model quality.

We evaluate Puzzle-75B-A9B on a broad suite of reasoning, coding, multilingual, long-context, and agentic benchmarks.

Despite substantial compression, the model retains strong downstream accuracy relative to the parent model across a wide range of tasks.

These results demonstrate that large hybrid MoE models can be substantially optimized for deployment efficiency while maintaining strong downstream capability.

Our model is publicly available on Hugging Face.

Paper@paper· Jul 9

[19/30] 282 Likes, 5 Comments, 4 Posts 2606.24937, cs․AI | cs․CL | cs․IR | cs․LG, 22 Jun 2026 🆕The Hitchhiker's Guide to Agentic AI: From Foundations to Systems Haggai Roitman

The Hitchhiker's Guide to Agentic AI is a comprehensive practitioner's reference for building autonomous AI systems.

The book covers the full stack from first principles to production deployment, organized around a central thesis: building great agentic systems requires understanding every layer of the pipeline, not just one.

The book opens with the LLM substrate -- transformer architecture, GPU systems, training and fine-tuning (SFT,LoRA, MoE), model compression, and inference optimization -- treated as essential foundations rather than the primary focus.

It then develops the alignment and reasoning layer: reinforcement learning from human feedback (RLHF), PPO, DPO and its variants, GRPO, reward modeling, and RL for large reasoning models including chain-of-thought and test-time scaling.

The second half is devoted to agentic AI proper.

Topics include agentic training and trajectory-based RL, retrieval-augmented generation (RAG and Agentic RAG), memory systems (in-context, external, episodic, and semantic), agent harness design and context management, and a taxonomy of agent design patterns.

Inter-agent coordination is covered in depth: the Model Context Protocol (MCP), agent skills and tool use, the Agent-to-Agent (A2A) communication protocol, and multi-agent architectures spanning centralized, decentralized, and hierarchical topologies.

The book concludes with agent development frameworks, agentic UI design, evaluation methodology for agentic tasks, and production deployment.

Each chapter pairs rigorous theoretical foundations with implementation guidance, code examples, and references to the primary literature.

Paper@paper· Jul 9

[25/30] 256 Likes, 12 Comments, 3 Posts 2607.02512, cs․LG | cs․AI | cs․CL, 02 Jul 2026 🆕Program-as-Weights: A Programming Paradigm for Fuzzy Functions Wentao Zhang, Liliana Hotsko, Woojeong Kim, Pengyu Nie, Stuart Shieber, Yuntian Deng

Many everyday programming tasks resist clean rule-based implementation, such as alerting on important log lines, repairing malformed JSON, or ranking search results by intent, and are increasingly outsourced to large language model APIs at the cost of locality, reproducibility, and price.

We propose fuzzy-function programming: compiling such a function from a natural-language specification into a compact, locally-executable neural artifact.

We instantiate this paradigm with Program-as-Weights (PAW), in which a 4B compiler trained on FuzzyBench, a 10M-example dataset we release, emits parameter-efficient adapters for a frozen, lightweight interpreter.

A 0.6B Qwen3 interpreter executing PAW programs matches the performance of direct prompting of Qwen3-32B, while using roughly one fiftieth of the inference memory and running at 30 tokens/s on a MacBook M3.

PAW reframes the foundation model from a per-input problem solver into a tool builder: invoked once per function definition, it produces a small reusable artifact whose subsequent calls per function application are cheap and offline.

Paper@paper· Jul 9

[29/30] 223 Likes, 3 Comments, 2 Posts 2606.17046, cs․RO | cs․CV | cs․LG, 22 Jun 2026 🆕Geometric Action Model for Robot Policy Learning Jisang Han, Seonghu Jeon, Jaewoo Jung, René Zurbrügg, Honggyu An, Tifanny Portela, Marco Hutter, Marc Pollefeys, Seungryong Kim, Sunghwan Hong

Generalist robot policies must follow user instructions while reasoning about how objects, cameras, and robot actions interact in the 3D physical world.

Recent vision-language-action models (VLAs) and video world-action models (WAMs) inherit strong semantic or temporal priors from large-scale foundation models, but they still operate primarily on 2D image frames or 2D-derived latent spaces, leaving implicit the 3D geometry required for contact-rich manipulation.

We propose the Geometric Action Model (GAM), a language-conditioned manipulation policy that directly repurposes a pretrained geometric foundation model (GFM) as a shared substrate for perception, temporal prediction, and action decoding.

GAM splits the GFM at an intermediate layer: the shallow layers serve as an observation encoder, and a causal future predictor inserted at the split layer forecasts future latent tokens conditioned on language, proprioception, and action history.

The predicted future tokens are then routed through the remaining GFM blocks for feature propagation and decoding, allowing a single backbone to produce both future geometry and actions.

This design equips the GFM with language-conditioned temporal world modeling through minimal architectural modification while preserving its rich geometric priors.

Across a broad suite of simulation and real-robot manipulation benchmarks, GAM is more accurate, more robust, faster, and lighter than current foundation-model-scale baselines.

Paper@paper· Jul 6

[28/30] 237 Likes, 46 Comments, 3 Posts 2607.01232, cs․LG | cs․CL, 02 Jul 2026 🆕Is One Layer Enough? Training A Single Transformer Layer Can Match Full-Parameter RL Training Zijian Zhang, Rizhen Hu, Athanasios Glentis, Dawei Li, Chung-Yiu Yau, Hongzhou Lin, Mingyi Hong

$Reinforcement learning (RL) has become a central component of post-training large language models (LLMs), yet little is understood about how RL adaptation is distributed across transformer layers. Existing approaches typically update all model parameters uniformly, implicitly assuming that every layer contributes similarly to the gains obtained during RL post-training. In this work, we challenge this assumption through a systematic layer-wise study of RL training. Surprisingly, we find that training a single transformer layer can recover most of the gains achieved by full-parameter RL training, and in some cases even surpass it. To quantify this phenomenon, we introduce the quantity layer contribution, which measures the fraction of full RL improvement recovered by training a layer in isolation. Across seven models spanning two model families (Qwen3, Qwen2.5), three RL algorithms (GRPO, GiGPO, Dr. GRPO), and multiple task domains including mathematical reasoning, code generation, and agentic decision-making, we observe a remarkably stable pattern: RL gains are highly concentrated in a small subset of, and in many cases even a single, transformer layers. More strikingly, the same structural pattern consistently emerges: high-contribution layers concentrate in the middle of the transformer stack, while layers near the input and output ends contribute substantially less. The resulting layer rankings remain strongly correlated across datasets, tasks, model families, and RL algorithms.$

Paper@paper· Jul 5

[7/30] 518 Likes, 6 Comments, 2 Posts 2606.30534, cs․CV, 30 Jun 2026 🆕Orca: The World is in Your Mind Yihao Wang, Yuheng Ji, Mingyu Cao, Yanqing Shen, Runze Xiao, Huaihai Lyu, Senwei Xie, Euan Liu, Klara Tian, Tianfeng Long, Yichi Zhang, Zhengliang Cai, Ruike Chen, Jifan Zhao, Ruochuan Shi, Z...

We introduce Orca, an initial instantiation of a general world foundation model.

Orca learns a unified world latent space from multimodal world signals and exposes it through multimodal readout interfaces.

Rather than optimizing isolated next-token, next-frame, or next-action prediction, we are centered on Next-State-Prediction modeling, offering a unified state-transition modeling route toward understanding, predicting, and acting upon the world.

Orca learns through two complementary paradigms: unconscious learning captures dense natural state transitions from continuous videos, and conscious learning models sparse meaningful state transitions by language-described events and VQA supervision.

For pre-training, we construct a large-scale world-learning inventory data, including 125K hours of video data and 160M event annotations.

After pre-training, Orca learns a unified world latent space.

To examine whether the learned latent supports downstream, we evaluate it by three representative downstream readouts: text generation, image prediction, and embodied action generation.

Orca's backbone is frozen, and only the lightweight modality-specific decoders are trainable.

Experiments show the scalability of the proposed paradigm and verify that stronger world latent enables stronger downstream readouts.

Orca outperforms similar-sized specialized baselines.

These results show that Orca, as a general world foundation model, presents a promising approach to understanding, predicting, and acting upon the world.

Finally, we discuss the current limitations, aiming to provide useful insights and inspiration for the community.

Paper@paper· Jul 5

[13/30] 329 Likes, 18 Comments, 4 Posts 2606.25996, cs․AI | cs․CL | cs․LG, 25 Jun 2026 🆕Autodata: An agentic data scientist to create high quality synthetic data Ilia Kulikov, Chenxi Whitehouse, Tianhao Wu, Yixin Nie, Swarnadeep Saha, Eryk Helenowski, Weizhe Yuan, Olga Golovneva, Jack Lanch...

We introduce Autodata, a general method that enables AI agents to act as data scientists who build high quality training and evaluation data.

We show how to train (meta-optimize) such a data scientist agent, so that it learns to create even stronger data.

We describe the overall formulation, and a specific practical implementation, Agentic Self-Instruct.

We conduct experiments on computer science research tasks, legal reasoning tasks and reasoning with mathematical objects, where we obtain improved results compared to classical synthetic dataset creation methods.

Further, meta-optimizing the data scientist agent itself delivers an even larger performance uplift.

Agentic data creation provides a way to convert increased inference compute into higher quality model training.

Overall, we believe this direction has the potential to change the way we build AI data.

Paper@paper· Jul 5

[17/30] 288 Likes, 3 Comments, 3 Posts 2606.24775, cs․CL | cs․DB | cs․IR, 23 Jun 2026 🆕Are We Ready For An Agent-Native Memory System? Wei Zhou, Xuanhe Zhou, Shaokun Han, Hongming Xu, Guoliang Li, Zhiyu Li, Feiyu Xiong, Fan Wu

Memory for large language model (LLM) agents has rapidly evolved from simple retrieval-augmented mechanisms into a data management system that supports persistent information storage, retrieval, update, consolidation, and dynamic lifecycle governance throughout agent execution.

Despite this evolution, existing evaluations still benchmark agent memory mainly through end-to-end task success metrics (e.g., F1, BLEU), while treating the underlying system as a monolithic black box.

As a result, critical system-level concerns, including operational costs, architectural trade-offs across memory modules, and robustness under dynamic knowledge updates, remain insufficiently explored.

In this paper, we present a systematic experimental study of agent memory from a data management perspective.

We propose an analytical framework that decomposes agent memory into four core modules: memory representation and storage, extraction, retrieval and routing, and maintenance.

Under this framework, we evaluate 12 representative memory systems and two reference baselines across five benchmark workloads spanning 11 datasets.

Our extensive end-to-end evaluation shows that no single architecture dominates across all scenarios; instead, effectiveness depends heavily on how well the memory structure aligns with the workload bottleneck.

Furthermore, through fine-grained ablation studies, we quantify their individual effects on representation fidelity, retrieval precision, update correctness, and long-horizon stability.

Finally, we reveal cost-performance trade-offs under realistic workloads, showing localized maintenance is more cost-efficient than global reorganization.

Based on these findings, we identify promising directions towards building truly agent-native memory systems.

The code is publicly available at https://github.com/OpenDataBox/MemoryData.

Paper@paper· Jul 5

[23/30] 261 Likes, 34 Comments, 2 Posts 2606.17384, cs․CV, 16 Jun 2026 🆕Improving and Evaluating Hand-Object Interaction Detection Ahmad Darkhalil, Dima Damen, David Fouhey

Understanding hands and the objects they interact with, both directly and through tools, is a key step for tasks ranging from action perception to 3D reconstruction and robotics.

Our paper provides several contributions to the Hand-Object Interaction (HOI) understanding literature: (1) HOI-DETR, a new framework that introduces hand-object and object-object interactions to the Co-DETR architecture to produce a state-of-the-art method; (2) a comprehensive HOI evaluation suite of 4 diverse datasets, including a video benchmark derived from the HD-EPIC dataset and fresh annotations that improve the Hands23 benchmark and (3) a trained checkpoint that significantly improves the state of the art across Hands23, HOIST, FineBio, and HD-EPIC, including mAP gains of over 20 percentage points on Hands23 and FineBio.

Our ablations confirm the contributions of each model component.

Paper@paper· Jul 5

[24/30] 254 Likes, 33 Comments, 3 Posts 2606.30616, cs․CL, 29 Jun 2026 🆕Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent Lei Bai, Zongsheng Cao, Yang Chen, Zhiyao Cui, Shangheng Du, Yue Fan, Shiyang Feng, Zijie Guo, Haonan He, Liang He, Xiaohan...

We introduce Agents-A1, a 35B Mixture-of-Experts Agentic Model that reaches trillion-parameter-level performance by scaling the agent horizon.

We investigate agent-horizon scaling from two perspectives: scaling long-horizon trajectories and scaling heterogeneous agent abilities.

To support this goal, we build a long-horizon knowledge-action infrastructure that connects external knowledge, actions, observations, and verifier outcomes, producing agentic trajectories with an average length of 45K tokens.

Based on this, we train Agents-A1 with a three-stage recipe.

First, we perform full-domain supervised fine-tuning to align the base model with broad agentic behaviors.

Second, we train domain-level teacher models to capture specialized expertise in each domain.

Third, we propose a multi-teacher domain-routed on-policy distillation with salient vocabulary alignment to improve knowledge transfer efficiency across different domains, unifying six heterogeneous domains into one deployable student model.

Agents-A1 achieves strong and broad performance for long-horizon agent benchmarks.

Compared with 1T-parameter model such as Kimi-K2.6 and DeepSeek-V4-pro, Agents-A1 achieves leading results on SEAL-0 (56.4), IFBench (80.6), HiPhO (46.4), FrontierScience-Olympiad (79.0), and MolBench-Bind (56.8), and remains highly competitive on SciCode (44.3), HLE (47.6) and BrowseComp (75.5).

We hope this work provides the community with a practical path for scaling the horizon using a 35B agent that can reach or match the performance of 1T models on long-horizon tasks.

Paper@paper· Jul 5

[26/30] 237 Likes, 3 Comments, 3 Posts 2606.14777, cs․CV | cs․AI, 10 Jun 2026 🆕JoyAI-VL-Interaction: Real-Time Vision-Language Interaction Intelligence Dingyu Yao, Junhao Zhou, Chenxu Yang, Chuanyu Qin, Haowen Hou, Zheming Liang, Congcong Wang, Yuhang Cao, Shenglong Ye, Shuai Xie, Shuhuan Gu...

Many moments in the real world do not wait for a user to ask.

A fire starts on a security monitor, an expression flickers across a video call, or a product a viewer wants flashes by in a livestream.

Yet today's large models remain mostly turn-based by design: they answer only when addressed, and even video-call apps that appear interactive still operate as question-answer systems, reacting only when polled or prompted.

We argue for a different paradigm: a model that is present in the world like a person.

It continuously watches what is happening now, decides on its own whether to speak or stay silent, interacts in real time, and delegates to a background model when the problem is hard.

To advance interaction models and their adoption across domains, we make two fully open-sourced contributions.

First, we release JoyAI-VL-Interaction, an 8B-scale, vision-first VL-interaction model.

The model makes the response decision internally, choosing each second to stay silent, respond, or delegate to a background model, and it excels at vision-triggered responsiveness and time awareness.

We pair it with a transferable training recipe, from which capabilities we never trained for emerge, such as guiding a shopper through changing app screens or improvising a lecture from a slide deck.

Second, we release a complete, deployable system built around that model.

The system streams any ongoing video into the model, making it genuinely present in the world.

All other components are pluggable, including ASR/TTS modules, memory, visualization UI, and a background brain that can connect to any API or agent.

Across six real-world scenarios, human raters prefer JoyAI-VL-Interaction over the in-app video-call assistants of Doubao and Gemini by a wide margin.

To our knowledge, this is the first open, vision-driven interaction model released together with its training recipe, data, and complete deployable system.

Paper@paper· Jul 5

[27/30] 234 Likes, 29 Comments, 1 Posts 2606.22283, cs․AR | cs․OS | cs․PF, 21 Jun 2026 🆕Apple Neural Engine: Architecture, Programming, and Performance Spencer H. Bryngelson