Mitigating the Length-Scaling Tax with Online Distillation
Length scaling during reinforcement-learning (RL) post-training is often viewed as a sign of improved reasoning ability, especially on difficult problems, but may also make respons...
Papers
Here the World in Stereo: Learning Dynamic Spatial Correspondence for Immersive Joint Video-Audio Generation
Recent joint video-audio generation models have achieved strong semantic correspondence and temporal synchronization. However, applications such as AR/VR and interactive gaming fur...
TauIndianBankBench: Procedure-Aware, Action-Oriented Evaluation of Tool-Using Conversational Agents
Many Indian banks serve customers through FAQ pages and scripted chatbots. Assistants based on language models could respond faster and carry out requests such as blocking a card, ...
JEPA-TTT: Persistent Test-Time Training of Latent World Models for Planning under Dynamics Shifts
World models enable agents to plan by predicting future states of the environment, but their predictions can become unreliable when test-time dynamics differ from those seen during...
Diffusion Editing with Soft Mask: Pixel Level Redo of Image and Video with Adjustable Strength
Diffusion models with prompt and reference image-guided editing have seen rapid progress, yet they remain too coarse for pixel-level control. One promising direction is to incorpor...
Scoring Higher, Answering Worse: Mitigating Reward Hacking in Rubric-Based RL via Protocol-Level Rubrics
Rubric-based reinforcement learning (Rubric-RL) trains language models where no verifier exists. A judge checks each criterion of a rubric, and the verdicts are aggregated into a r...
Learning from Runtime Feedback through Failure-Bank Self-Evolution for Vision-Language-Action Models
Vision-language-action (VLA) models generalize broadly across robotic manipulation tasks, but complex environments require balancing task success with unintended contact. Runtime s...
A Missing Piece for Trustworthy AI Reviewers: From Benchmarking Rhetorical Robustness to SciCore Review
AI reviewers can assign different judgments to manuscripts that report the same science in different wording, potentially rewarding rhetorical optimization over scientific improvem...
Personalized Image Generation with Reasoning and Reflection
Personalized image generation has remained narrowly focused on conditional synthesis from curated visual exemplars, rather than capturing who a user is. In practice, however, a use...
Honeycomb: Constant-Size Scene Memory Representation for Video World Models
Video world models require persistent scene memory to maintain consistency during long-horizon video generation. Existing spatial memories accumulate RGB observations or latent fea...
Making LLMs Say What They Think: Measuring and Improving CoT-Interpretability Alignment
Chain-of-thought (CoT) traces often serve as a proxy for how Large Language Models (LLMs) arrive at their answers. However, growing evidence shows that models' CoT often fails to r...
Decision-Oriented Recommendation Reranking: An Empirical Study of Jev
Large language models (LLMs) have shown promise for recommendation reranking, but their use introduces an important tradeoff between recommendation quality and serving efficiency. ...
Agent Error Dataset: Scaling 50,000 Error--Diagnosis Pairs for Failure Analysis and Error-Aware Post-Training
An unsuccessful LLM agent rollout contains more information than its final reward: the observations available to the agent, the actions it chose, and the environment's responses. R...
Thinking Outside the Box: Can Language Models Rely on External Guidance Selectively?
Agent harnesses often improve language models with human-designed workflows, but as models grow more capable, unreliable guidance can increasingly constrain their execution. We cal...
JevSpawn: Adaptive Agentic Inference through Compositional Action Spaces
LLM agents generate intermediate reasoning and actions token by token, making extended interactions slow and computationally expensive. Jev-style models offer fast probabilistic pr...
Architect-Ant: Editable Automatic Furnishing of Architectural Floor Plans
Furnished floor plans support real-estate visualization, interior design, and architectural workflows, yet automatic furnishing remains challenged by limited real-world data and th...
SkillSeek: Revisiting Agent Skill Retrieval at Marketplace Scale
Anthropic's Agent Skills package reusable procedural know-how for an LLM agent into SKILL.md directories, and open-source aggregations have grown past 230,000 skills, making select...
Beyond the Current Scene: Event-Referential Grasping with Active View Selection
A robot that observes people interacting with objects should be able to carry out later requests that refer back to those interactions. Such requests may specify a grasp target by ...
PatchHolmes: Agentic Patch Retrieval via Listwise Selection
Patch retrieval, the task of finding the commit that fixes a known vulnerability, is the foundation of vulnerability management workflows, yet 60% to 63% of CVEs in the major advis...
DexPolicy: Scheduled Exploration for Trajectory-Guided Dexterous Manipulation
Reinforcement learning (RL) for dexterous manipulation must discover finger-object contacts and then control the object precisely; the action noise that serves the first goal can i...
RSIGame: Autonomous Agentic Game Development with Recursive Self-improvement
Recent advances in large language models have made automatic game generation increasingly feasible, yet reliably improving generated games beyond a playable version remains challen...
CoEvoWhen: Policy-Tool Coevolution for Ultra-Long Video Temporal Grounding
Ultra-long video temporal grounding requires balancing long-range evidence search with fine-grained event understanding under a limited visual budget, yet existing agentic methods ...
RoboCoach: World Models as Active Coaches for Compositional Robot Skills
Long-horizon robot manipulation reuses skills across many task compositions, but improving these compositions with additional end-to-end demonstrations is costly. A practical self-...
GroundAnything: Reconciling Parallel Decoding with Precise Visual Grounding at Flash Speed
Autoregressive (AR) grounding models serialize spatial predictions, introducing sequential latency and imposing a causal order on output tokens. We view grounding as visual evidenc...