Trial by Trace
Benchmarking AI agent harnesses for reliability and failure attribution. TraceTrial-Bench examines 95 tasks across four harnesses and nine model configurations.
From the lab
Milestones
Reports, papers, and moments that move our work forward.
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Report dated
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Article published
TraceLite AI in InfoQ’s AICon Shenzhen recap
InfoQ’s conference recap lists TraceLite AI among the organizations represented by speakers at AICon Shenzhen 2026.
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Proceedings
SimRPD
Optimizing recruitment proactive dialogue agents through simulator-based data evaluation and selection.
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Proceedings
Look Less, Reason More
Rollout-Guided Adaptive Pixel-Space Reasoning.
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Conference year
MM-VeriRubric: Verifiable Evidence-Grounded Rubrics for Multimodal Reward Models
Grounding multimodal reward-model rubrics in task-relevant visual evidence and verifying their consistency.
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Conference year
Disentangled Privileged States for Simulator-Assisted Multi-Turn Dialogue Policy Training
Separating privileged state tracking from simulated user utterances to support multi-turn dialogue policy training.
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Conference year
Absorbing Gradient Conflicts: Modeling Semantic Variance via Kent Distributions for Cross-Modal Hashing
Modeling class proxies as Kent distributions on the hypersphere so that conflicting multi-label gradients are absorbed by directional variance instead of shifting the proxy.
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Conference year
Select Less, Reason More: Prioritizing Evidence Purity for Video Reasoning
Selecting relevant frames and resampling visual evidence for video reasoning.
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Conference year
CausalStep
A Benchmark for Explicit Stepwise Causal Reasoning in Videos.
Featured work
Research
Papers
MM-VeriRubric: Verifiable Evidence-Grounded Rubrics for Multimodal Reward Models
Most multimodal reward models emit only an outcome reward, which is sparse and misses the structure of human preference. MM-VeriRubric builds training data for multimodal rubric generation at scale: task-aware visual clue extraction grounds rubrics in task-relevant image evidence, and rubric-aligned rejected responses with controlled error dimensions let each rubric be verified through consistency checking. The verified data trains both a rubric generator and a rubric-based reward model, improving preference judgment by 16.4% on VL-RewardBench and Best-of-5 selection by 13.1% on MMMU-Pro.
Disentangled Privileged States for Simulator-Assisted Multi-Turn Dialogue Policy Training
Simulator-assisted reinforcement learning is a practical way to train multi-turn dialogue agents, but policy optimization gets misled when the simulated user gives inconsistent feedback across long trajectories. DPS decouples task-conditioned privileged state tracking from surface utterance realization; those privileged states stay hidden from the agent but are used during training to derive dense, state-aware process rewards that complement sparse outcome signals. The work also introduces RecruitmentBench, a persuasion dialogue benchmark grounded in anonymized real-world logs, and reports improved measured simulator fidelity and stronger fine-tuned policy performance.
Absorbing Gradient Conflicts: Modeling Semantic Variance via Kent Distributions for Cross-Modal Hashing
Proxy-based deep cross-modal hashing usually treats each class proxy as a fixed point in the embedding space. With multi-label data, co-occurring labels pull that point in conflicting directions, causing gradient contention and proxy collapse. KDPH instead models each proxy as an anisotropic Kent distribution on the hypersphere: the proxy keeps a stable mean direction and absorbs conflicting gradients by adjusting its directional variance, while a loss built on the Cayley transform keeps the distribution parameters orthogonal during training. It is the first framework to bring Kent distributions into cross-modal hashing; on three benchmark datasets it mitigates proxy collapse and oscillation and outperforms state-of-the-art methods.
SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection
Proactive dialogue agents steer recruitment conversations toward concrete business outcomes, but training them is bottlenecked by the scarcity of high-quality, goal-oriented domain data. SimRPD is a three-stage framework: a high-fidelity user simulator synthesizes large-scale multi-turn conversations, a Chain-of-Intention evaluation framework assesses the simulator and selects high-quality data using both global-level and instance-level metrics, and the agent is then trained on that selection. In a real-world recruitment scenario SimRPD outperforms existing simulator-based data selection strategies.
Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning
Pixel-level visual operations help Vision-Language Models handle fine-grained detail, but they are routinely overused — costing efficiency and pulling attention toward irrelevant regions. This paper proposes the first framework for adaptive pixel reasoning, which decides per query whether a pixel operation is warranted: operation-aware supervised fine-tuning establishes baseline competence, then a rollout-guided reinforcement learning stage uses feedback from the model's own responses to learn when to invoke tools. The result reaches 73.4% accuracy on HR-Bench 4K at a tool usage ratio of only 20.1% — 66.5% less tool use than prior methods, with higher accuracy.
Select Less, Reason More: Prioritizing Evidence Purity for Video Reasoning
Uniform frame sampling dilutes evidence in long videos, and existing pixel-space video agents lack reward mechanisms that enforce evidence purity or let them look beyond pre-sampled frames. This work introduces EARL, an evidence-aware reinforcement learning framework that turns the model into an active interrogator of evidence: it selects the most relevant frames and re-samples locally around them to recover fine-grained temporal detail. Across five video reasoning benchmarks the EARL-trained 7B model sets a new state of the art among open-source Video LLMs, reaching 59.8% on LongVideoBench, 69.0% on MVBench and 64.9% on VideoMME.
CausalStep: A Benchmark for Explicit Stepwise Causal Reasoning in Videos
Existing video benchmarks mostly test shallow understanding and let models lean on global context, so they cannot tell whether a model really reasons step by step about cause and effect. CausalStep segments each video into causally linked units and enforces a strict stepwise question-answer protocol: questions must be answered in sequence, which blocks shortcut solutions, and every question carries distractors built from an error-type taxonomy. The benchmark has 100 videos across six categories and 1,852 multiple-choice QA pairs, scored with seven diagnostic metrics. Experiments with leading proprietary and open-source models, alongside human baselines, show a significant gap between current models and human-level stepwise causal reasoning.
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Blog
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Notes
What we are working on, and what we plan to build next.
Roadmap / Environment
What is an agent's environment made of?
We want to describe an environment the way we describe a model: by listing the factors that change what the right move is.
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Roadmap / Evaluation
Evaluating the harness, not just the model
The same model can behave very differently inside different agent harnesses. We want that difference to be measurable.
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Roadmap / Training data
Turning failures into training data
Agents fail all the time on real tasks, and most of those failures are thrown away. We want to keep the useful ones.
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