5 papers · 1 filter
Let the Library Speak: Self-Advertised Method Selection for Formal Proving
Xiaopeng Yuan, Suijin Wang, Yanli Wang +5
LLM-based formal provers can retrieve relevant lemmas and prior proofs, but relevance alone does not say whether a mathematical method can be used on the current theorem. A method…
Composing Task-specific Agent Harnesses at Test Time with Reusable Primitives
Peng Kuang, Haibo Jin, Dehao Wu +4
Agent harnesses govern how large language models (LLMs) gather context, invoke tools, verify results, preserve state, and terminate, largely affecting agent performance. However, t…
ANTMAN: Adaptive Need Tracking for Multi-Agent Navigation in Large Information Spaces
Jerry Wang, Haibo Jin, Xiaopeng Yuan +2
Information-seeking agents increasingly operate over information spaces that are too large to process exhaustively. Yet many multi-agent systems organize computation around static…
KV-PRM: Efficient Process Reward Modeling via KV-Cache Transfer for Multi-Agent Test-Time Scaling
Peng Kuang, Haibo Jin, Xiaoyu Han +5
Process Reward Models (PRMs) have been proven to be highly effective in guiding test-time scaling (TTS) methods, which significantly boost the capabilities of LLM-based multi-agent…
Closing the Loop on Latent Reasoning via Test-Time Reconstruction
Xiaopeng Yuan, Haibo Jin, Ye Yu +4
Recent work moves intermediate reasoning from natural-language traces into latent or cache-level representations to reduce token overhead and avoid a discrete communication bottlen…