arXiv wire · cs.LG · cs.AI · vision
Agents win when they can deliberate slowly; this scales fast-action settings (robotics, games) where they must act. Builds the missing real-time eval infra.
By Pranjal Aggarwal, Sean Welleck
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LLMs/agents 71 · multimodal/vision 64 · coding agents 25 · finance/trading 19 · hot RL 13 · cross-field 8
GENKO 原稿 | arXiv AI digest — Thu 29 Oct 2026
Window: latest arXiv submission day in feed, 27 Oct 2026 (UTC). Strict last-24h was empty — feed lag; fell back to newest day present.
Pipeline: fetched 200 · unique 200 (0 dupes) · picked 9.
TOP PICKS
1) Gym-Anything-RT: Scaling Real-Time Environments for Vision-Language Agents
Agents win when they can deliberate slowly; this scales fast-action settings (robotics, games) where they must act. Builds the missing real-time eval infra. — Pranjal Aggarwal, Sean Welleck. arxiv.org/abs/2610.37567 [agents, vision]
2) Guided-RLVR: Guided Exploration in Reinforcement Learning with Verifiable Rewards
RLVR lifts LLM reasoning, but exploration collapses late in training; guided exploration sustains it. Directly relevant to how reasoning models get trained. — Bo Yang, Zishuai Zhang, Weihua Li et al. arxiv.org/abs/2610.37041 [RL, LLM]
3) FeatureArc: Benchmarking Coding Agents on Evolving Features
Real features grow and change; most agent benches are static. Tests coding agents on extending prior code under new requirements. — Yunlong Wang, Alyssa Wu, Cuiyun Gao et al. arxiv.org/abs/2610.37434 [coding agents]
4) Test-Time Communication in Trading Agent Swarms
Trading gives agents no shared objective and no fast feedback — hard case for multi-agent comms. Studies when agents should talk at inference. — Avid Fayaz, Harry Zhang, Håvard Alstadheim et al. arxiv.org/abs/2610.37448 [finance, agents]
5) LiveSearch: Questions as Executable Verifier Code for a Live Benchmark of Professional-Source Financial Search Agents
Finance answers must be current; benchmarks agents on live, source-grounded retrieval with verifiable queries instead of static QA. — Sinuo Wang, Wentao Zhao, Jiacheng Lu et al. arxiv.org/abs/2610.37166 [finance, agents]
6) Metacognitive Decoding: Efficient Inference for Diffusion Language Agents
Diffusion LMs emit tokens in parallel; metacognitive decoding spends compute and text only where needed for agent loops. — Mingyu Zhao, Shuhang Lin, Ligong Han et al. arxiv.org/abs/2610.37642 [agents, LLM]
7) Concordat: Typed Sessions for LLM Agents
Agents now take consequential actions (refunds, cancellations); typed session rules enforce who-may-do-what before the call. — Milad Sikaroudi. arxiv.org/abs/2610.37461 [agents, safety]
8) V-LUMEN: Visual Lookup Memory for Embedding Scaling in Vision-Language Models
Scales VLM capacity via lookup memory instead of deeper/wider dense models — cheaper capacity scaling for multimodal. — Miso Choi, Daekeun Kim, Eunji Kim et al. arxiv.org/abs/2610.37019 [multimodal]
9) Uncovering Shortcut Amplification in Video RLVR through Perceptual Uncertainty Collapse
Video RLVR gains partly ride on shortcuts: perceptual uncertainty collapses and amplifies them. Matters for anyone shipping RL-trained VLMs. — Jiafeng Liang, Xianhao Ou, Tao Ren et al. arxiv.org/abs/2610.37211 [RL, vision]
ALSO TODAY (200 in window): LLMs/agents 71 · multimodal/vision 64 · coding agents 25 · finance/trading 19 · hot RL 13 · cross-field 8.
Honorable mentions:
- TRIDENT-Seq (Ant International): label-efficient payment risk modeling via prefix-future contrast. arxiv.org/abs/2610.37365 [finance]
- TeamGym: team decision-making by AI agents with distributed info. arxiv.org/abs/2610.37571 [agents]
- VideoLVR: latent visual reasoning over grounded video evidence. arxiv.org/abs/2610.37613 [vision]
- AI-assisted CNOT decompositions for quantum permutations. arxiv.org/abs/2610.37045 [cross-field]