The Linux Foundation announced the contribution of TRACE (Trust, Runtime Attestation and Compliance Evidence), a hardware-backed runtime evidence specification for AI agents, developed collaboratively…
Researchers propose TRACE, a transition-aware residual control framework that improves multi-objective materials discovery with LLM agents by treating evaluated edits as feedback units. In a controlle…
A new framework called TRACE (Trustworthy Retrieval-Augmented Conversational Engine) improves constraint-aware recommendation in public service chatbots by strengthening retrieval, according to a stud…
Researchers introduced TRACE, a multi-layer benchmark for diagnosing drift in human-AI control loops, built from 1,918 drifted traces derived from the ALFRED household-task benchmark. The dataset labe…
Researchers propose TRACE, a trajectory-based safety patch learning framework that recovers safety alignment in fine-tuned large language models without re-running full alignment or sacrificing task u…
TRACE, a novel dense credit-assignment framework for reinforcement learning, dramatically improved agent performance on the BrowseComp-Plus benchmark, boosting Qwen3-4B from 7.2 to 35.6 and Qwen3-30B-…
TRACE (Typed Reasoning And Commitment Evidence), a new framework for recording AI decision-making processes in a structured and auditable way, aims to introduce transparency and accountability into ho…
Stanford researchers introduced TRACE, an open-source system that diagnoses recurring agent failures and trains targeted capabilities via synthetic reinforcement learning environments, achieving a hig…
Researchers introduced TRACE, a new benchmark for evaluating conversational AI systems that use external tools, revealing that all current state-of-the-art evaluation frameworks fail to detect critica…
TRACE, an open-source Python library for self-organizing long-term memory in LLM agents, was released on PyPI. It organizes conversation history into a hierarchical B+Tree to enable efficient, multi-p…
Researchers introduced TRACE, a training-free framework that stabilizes reconstruction trajectories in imaging inverse problems by coupling adjacent intermediate states. The method, which approximates…
Researchers have developed TRACE, a trajectory-aware AI agent that uses large language model reasoning to optimize drug lead compounds by treating tool selection as a sequential decision-making proces…