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| title: README | |
| emoji: 馃攳 | |
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| # TraceLite AI | |
| An academic-driven open research team working on the building blocks of AI agent evaluation and post-training: **forensics, attribution, environments, rewards, and data generation**. Each component can be used on its own for model and harness selection, training, and pre-release acceptance testing, and together they form a framework for continual self-improvement. | |
| [Website](https://tracelite.ai) 路 [GitHub](https://github.com/TraceLite-AI) 路 [Research timeline](https://tracelite.ai/timeline.html) 路 contact@tracelite.ai | |
| ## Trial by Trace 路 TraceTrial-Bench | |
| When an AI agent fails, was it the model or the harness around it? TraceTrial-Bench scores execution traces in two layers, task outcome and harness reliability, across 95 mechanism-oriented tasks, 4 harnesses (Claude Code, Codex, OpenClaw, DeepSeek Harness) and 9 models, 3,420 runs in total. | |
| - 馃攳 [Interactive report and leaderboard (Space)](https://huggingface.co/spaces/TraceLite-AI/Trial-by-Trace) | |
| - 馃捇 [Code and tasks](https://github.com/TraceLite-AI/TraceTrial-Bench) | |
| - 馃搫 [Technical report](https://tracelite.ai/trial-by-trace.html) | |
| ## Papers (2026) | |
| | Paper | Venue | | |
| |---|---| | |
| | [MM-VeriRubric: Verifiable Evidence-Grounded Rubrics for Multimodal Reward Models](https://tracelite.ai/papers.html) | EMNLP 2026 | | |
| | [Disentangled Privileged States for Simulator-Assisted Multi-Turn Dialogue Policy Training](https://tracelite.ai/papers.html) | EMNLP 2026 Findings | | |
| | [Absorbing Gradient Conflicts: Modeling Semantic Variance via Kent Distributions for Cross-Modal Hashing](https://arxiv.org/abs/2608.24010) | IJCAI 2026 | | |
| | [SimRPD: Optimizing Recruitment Proactive Dialogue Agents through Simulator-Based Data Evaluation and Selection](https://aclanthology.org/2026.acl-industry.95/) | ACL 2026 路 Oral | | |
| | [Look Less, Reason More: Rollout-Guided Adaptive Pixel-Space Reasoning](https://aclanthology.org/2026.acl-long.225/) | ACL 2026 | | |
| | [Select Less, Reason More: Prioritizing Evidence Purity for Video Reasoning](https://openaccess.thecvf.com/content/CVPR2026/html/Li_Select_Less_Reason_More_Prioritizing_Evidence_Purity_for_Video_Reasoning_CVPR_2026_paper.html) | CVPR 2026 | | |
| | [CausalStep: A Benchmark for Explicit Stepwise Causal Reasoning in Videos](https://arxiv.org/abs/2507.16878) | AAAI 2026 路 Oral | | |
| Full list: [tracelite.ai/papers.html](https://tracelite.ai/papers.html) | |