TRACE: Trajectory-Aware Comprehensive Evaluation for Deep Research Agents

• URL: http://arxiv.org/abs/2602.21230v1
• Date: Thu, 05 Feb 2026 13:28:57 GMT
• Title: TRACE: Trajectory-Aware Comprehensive Evaluation for Deep Research Agents
• Authors: Yanyu Chen, Jiyue Jiang, Jiahong Liu, Yifei Zhang, Xiao Guo, Irwin King,
• Abstract summary: Trajectory-Aware Comprehensive Evaluation (TRACE) is a framework that holistically assesses the entire problem-solving trajectory.<n>Our contributions include the TRACE framework, its novel metrics, and the accompanying DeepResearch-Bench with controllable complexity.
• Score: 51.30998248590416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The evaluation of Deep Research Agents is a critical challenge, as conventional outcome-based metrics fail to capture the nuances of their complex reasoning. Current evaluation faces two primary challenges: 1) a reliance on singular metrics like Pass@1, creating a "high-score illusion" that ignores the quality, efficiency, and soundness of the reasoning process; and 2) the failure of static benchmarks to quantify crucial attributes like robustness and latent capability. To address these gaps, we introduce TRACE (Trajectory-Aware Comprehensive Evaluation), a framework that holistically assesses the entire problem-solving trajectory. To counter the "high-score illusion", we propose a Hierarchical Trajectory Utility Function that quantifies process efficiency and cognitive quality, including evidence grounding, alongside accuracy. To measure deeper attributes, TRACE introduces a Scaffolded Capability Assessment protocol, quantifying an agent's latent ability by determining the minimum guidance needed for success. Our contributions include the TRACE framework, its novel metrics, and the accompanying DeepResearch-Bench with controllable complexity. Experiments show TRACE delivers a granular ranking that uncovers critical trade-offs between agent accuracy, efficiency, and robustness entirely missed by singular metrics.

Related papers

• DREAM: Deep Research Evaluation with Agentic Metrics [21.555357444628044]
  We propose DREAM (Deep Research Evaluation with Agentic Metrics), a framework that makes evaluation itself agentic.<n> DREAM structures assessment through an evaluation protocol combining query-agnostic metrics with adaptive metrics generated by a tool-calling agent.<n>Controlled evaluations demonstrate DREAM is significantly more sensitive to factual and temporal decay than existing benchmarks.
  arXiv  Detail & Related papers  (2026-02-21T19:14:31Z)
• Search-R2: Enhancing Search-Integrated Reasoning via Actor-Refiner Collaboration [49.9937230730202]
  We propose Search-R2, a novel Actor-Refiner collaboration framework that enhances reasoning through targeted intervention.<n>Our approach decomposes the generation process into an Actor, which produces initial reasoning trajectories.<n>We show that Search-R2 consistently outperforms strong RAG and RL-based baselines across model scales.
  arXiv  Detail & Related papers  (2026-02-03T15:32:09Z)
• Beyond the Final Answer: Evaluating the Reasoning Trajectories of Tool-Augmented Agents [22.781523439717223]
  A proper evaluation of an agent's performance must go beyond the final answer to also assess the problem-solving trajectory.<n>We introduce TRACE, a framework for the multi-dimensional evaluation of tool-augmented LLM agent performance.<n>Our results confirm that TRACE accurately evaluates these complex behaviors in a scalable and cost-effective manner.
  arXiv  Detail & Related papers  (2025-10-03T09:19:15Z)
• CORE: Full-Path Evaluation of LLM Agents Beyond Final State [2.0391237204597368]
  Existing agentic benchmarks often reduce evaluation to a binary judgment of the final state.<n>We propose a framework based on deterministic finite automata that encodes tasks as sets of valid tool-use paths.<n>We introduce CORE, a suite of five metrics, namely Path Correctness, Path Correctness - Kendall's tau Composite, Prefix Criticality, Harmful-Call Rate, and Efficiency.
  arXiv  Detail & Related papers  (2025-09-25T10:49:35Z)
• Automatic Failure Attribution and Critical Step Prediction Method for Multi-Agent Systems Based on Causal Inference [8.823529310904162]
  Multi-agent systems (MAS) are critical for automating complex tasks, yet their practical deployment is hampered by the challenge of failure attribution.<n>We introduce the first failure attribution framework for MAS grounded in multi-granularity causal inference.
  arXiv  Detail & Related papers  (2025-09-10T15:22:00Z)
• Personality as a Probe for LLM Evaluation: Method Trade-offs and Downstream Effects [0.6087817758152709]
  We present a systematic study of personality control using the Big Five traits.<n>Trait-level analysis shows openness as uniquely challenging, agreeableness as most resistant to ICL.<n>Experiments on Gemma-2-2B-IT and LLaMA-3-8B-Instruct reveal clear trade-offs.
  arXiv  Detail & Related papers  (2025-09-05T04:19:15Z)
• Towards Evaluting Fake Reasoning Bias in Language Models [47.482898076525494]
  We show that models favor the surface structure of reasoning even when the logic is flawed.<n>We introduce THEATER, a benchmark that systematically investigates Fake Reasoning Bias (FRB)<n>We evaluate 17 advanced Large Language Models (LRMs) on both subjective DPO and factual datasets.
  arXiv  Detail & Related papers  (2025-07-18T09:06:10Z)
• Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL [86.0987896274354]
  We first identify a fundamental pattern, self-excitation, as the primary cause of Q-value estimation divergence in offline RL. We then propose a novel Self-Excite Eigenvalue Measure (SEEM) metric to measure the evolving property of Q-network at training. For the first time, our theory can reliably decide whether the training will diverge at an early stage.
  arXiv  Detail & Related papers  (2023-10-06T17:57:44Z)
• Ladder-of-Thought: Using Knowledge as Steps to Elevate Stance Detection [73.31406286956535]
  We introduce the Ladder-of-Thought (LoT) for the stance detection task. LoT directs the small LMs to assimilate high-quality external knowledge, refining the intermediate rationales produced. Our empirical evaluations underscore LoT's efficacy, marking a 16% improvement over GPT-3.5 and a 10% enhancement compared to GPT-3.5 with CoT on stance detection task.
  arXiv  Detail & Related papers  (2023-08-31T14:31:48Z)
• Goodhart's Law Applies to NLP's Explanation Benchmarks [57.26445915212884]
  We critically examine two sets of metrics: the ERASER metrics (comprehensiveness and sufficiency) and the EVAL-X metrics. We show that we can inflate a model's comprehensiveness and sufficiency scores dramatically without altering its predictions or explanations on in-distribution test inputs. Our results raise doubts about the ability of current metrics to guide explainability research, underscoring the need for a broader reassessment of what precisely these metrics are intended to capture.
  arXiv  Detail & Related papers  (2023-08-28T03:03:03Z)
• WSSOD: A New Pipeline for Weakly- and Semi-Supervised Object Detection [75.80075054706079]
  We propose a weakly- and semi-supervised object detection framework (WSSOD) An agent detector is first trained on a joint dataset and then used to predict pseudo bounding boxes on weakly-annotated images. The proposed framework demonstrates remarkable performance on PASCAL-VOC and MSCOCO benchmark, achieving a high performance comparable to those obtained in fully-supervised settings.
  arXiv  Detail & Related papers  (2021-05-21T11:58:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.