WARP: Weight-Space Analysis for Recovering Training Data Portfolios
Foundation models are routinely released to the public, yet the data recipes used to train them -- such as domain mixture weights that determine how different…
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AutoMem: Automated Learning of Memory as a Cognitive Skill
Memory expertise is a learned skill: knowing what to encode, when to retrieve, and how to organize knowledge--a capacity known in cognitive science as metamemory. We…
DuoMem: Towards Capable On-Device Memory Agents via Dual-Space Distillation
Large Language Model (LLM)-based agents can solve complex procedural tasks by interacting with environments over multiple turns, but this ability typically depends on large models, long…
Logit-Contribution Scoring Identifies Non-Literal Retrieval Heads
In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pasting them. Identifying which attention heads…
AGVBench: A Reliability-Oriented Benchmark of Data Augmentation for Vein Recognition
Vein recognition is a secure biometric technology often constrained by limited annotated data and imaging variations. While data augmentation mitigates this, strategies designed for natural images…
Transferability for General Reasoning: An Automated Curriculum for Multi-Domain RLVR
Reinforcement learning with verifiable rewards (RLVR) has been extended from single-domain training to multi-domain reasoning suites spanning mathematics, programming, and science. However, the training curriculum (how…
InstanceControl: Controllable Complex Image Generation without Instance Labeling
Controllable image generation methods, such as ControlNet, have demonstrated a remarkable capacity to introduce visual conditions(e.g., depth maps) to guide image generation. However, these methods often…
Breaking Failure Cascades: Step-Aware Reinforcement Learning for Medical Multimodal Reasoning
Recent multimodal large language models have shown great promise in clinical image reasoning, but existing post-training pipelines remain predominantly outcome-centric, relying on final answer correctness or…
EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments
Autonomous agents are increasingly expected to improve executable policies through feedback, yet existing evaluations often collapse this process into a final score or confound it with…
AgenticSTS: A Bounded-Memory Testbed for Long-Horizon LLM Agents
Memory for a long-horizon LLM agent is a contract about what each future decision is allowed to see. The simplest contract appends past observations, tool calls,…