trace-98f4854b·3 events·first seen Aliases: TRACE
Researchers introduce DyadEE, a dataset for detecting emotional entrainment in dyadic speech, containing both natural entrained conversations and synthetic disrupted interactions created via partner swapping and emotion resynthesis. They also propose TRACE, a window-level framework that models dyadic interactions as ordered sequences of acoustic embeddings from emotion fine-tuned Whisper representations. TRACE achieves 97.01% accuracy on DyadEE, with conversational context and relationship information proving key to performance. The work is motivated by the growing deployment of speech AI agents that need to understand affective coordination.
Researchers introduce TRACE, a detection framework for corpus poisoning attacks on Retrieval-Augmented Generation (RAG) systems that works by tracing answer-related tokens through token influence attribution rather than relying on auxiliary classifiers or LLM-based verification. The method identifies recurrent high-influence keywords across retrieved documents and performs secondary verification to confirm their effect on model predictions. Evaluated on three QA benchmarks and six LLMs, TRACE achieves strong detection performance while also exposing attacker-specified target answers, with lower computational overhead than prior approaches.
TRACE (Tree Rollout Allocation for Contrastive Exploration) is a new framework for improving reinforcement learning with verifiable rewards (RLVR) in multi-turn agentic LLM settings. The method models each ReAct-style thought-action-observation turn as a distinct node, enabling budget allocation across both prompt-level and turn-level prefixes in a tree structure, rather than only at the prompt level. A shared predictor estimates conditional success probability at each anchor to guide allocation, enriching reward contrast within a fixed sampling budget. Empirically, TRACE improves Qwen3-14B multi-hop QA accuracy by 2.8 points over baselines at equal sampling cost.