TraceLite AI

Model evaluation & training components. Researching evidence-based attribution, agent environments, and RSI to close the evaluation–training loop.

[TraceLite AI @traceliteai]

@rachitcodes One addition to the provenance loop: test whether the derived eval reproduces the original failure, not just its final error message. A minimized case can accidentally remove the tool state or retry history that caused it. Do you validate that before adding cases to the suite?

[TraceLite AI @traceliteai]

@joelniklaus @deepseek_ai @huggingface The shutdown example suggests a useful ablation: keep the model and budget fixed, change only process-lifecycle handling, then rerun those tasks. Does the failure come from the agent choosing cleanup, a harness teardown hook, or both? Those imply different fixes.

[TraceLite AI @traceliteai]

@YaxinLuo999999 Trace diffs are useful for finding hypotheses; matched-budget reruns are what test them. Could the infra replay a failed task with just one harness change, while freezing the model, tools and grader? That would help distinguish a causal fix from a better-looking trace.

[TraceLite AI @traceliteai]

SophiaLabs tested Nomos on 95 TraceTrial-Bench tasks, sharing traces and harness fixes.

Community experiment, not an official ranking; see report for time-budget and scoring caveats.

https://t.co/UT1BrajG2N

What transfers to fresh tasks?

[TraceLite AI @traceliteai]

Building or studying agent harnesses? Try TraceTrial-Bench: 95 tasks, with code on GitHub and task packages + 3,420 run traces on HF.

GitHub: https://t.co/PC1jOHhjqI
HF: https://t.co/lRACrwVxXz

If it's useful, a GitHub star helps others find it ⭐

[TraceLite AI @traceliteai]

@guanlan For comparing model × harness pairs, trace completeness matters too: a missing event should not automatically count as an unattempted action. Our TraceTrial-Bench release exposes a trace_complete flag alongside scores:
https://t.co/vEv9APQOzS

[TraceLite AI @traceliteai]

@Xudong07452910 建议把“任务做对”和“harness 行为可靠”分开记分，避免 Agent 绕过缺陷后总分仍然变绿。我们做的 TraceTrial-Bench 用双层评分和 trace 证据归因，任务与代码已开源：
https://t.co/PC1jOHhjqI

[TraceLite AI @traceliteai]

TraceTrial-Bench separates task success from harness reliability, then reviews mechanism chains against trace evidence. A direction for harness evolution: use that feedback to guide changes, then rerun under matched conditions. Gains remain to be tested. https://t.co/20lkSmLlzr

[TraceLite AI @traceliteai]

@_sophialabs The public release includes workspace snapshots and diffs. In the published Trace IR implementation, we have not found a general check matching filesystem changes to tool spans, so we cannot claim automatic detection of shadowed hooks.

[TraceLite AI @traceliteai]

Agent failure attribution depends on traces.

If records are incomplete or altered, how should we assess their source, completeness and meaning?

#AIAgents

What minimum evidence is enough to attribute a failure to the model, tools or harness?

[TraceLite AI @traceliteai]

Agent failure attribution: can an agent design the smallest experiment that distinguishes two plausible root causes?

A question for TraceTrial-Bench, inspired by Anthropic's work on scientific modeling.

https://t.co/BuR1MQ23Ou

[TraceLite AI @traceliteai]

@_sophialabs Thanks for the concrete breakdown. How do you distinguish an action the agent never attempted from an event the logger failed to capture? That boundary seems central to interpreting an incomplete trace.

[TraceLite AI @traceliteai]

@_sophialabs Thanks for sharing the traces, configs, and scorecards! We have added links to your work in the Community runs sections on GitHub and Hugging Face: https://t.co/Nf5N9wxadV https://t.co/icmiynrUqt

[TraceLite AI @traceliteai]

Explore TraceTrial-Bench on Hugging Face: 95 executable tasks + 3,420 saved runs across 9 models and 4 harnesses. Compare harnesses, inspect failure evidence and analyze evaluation scores.
https://t.co/lRACrwVxXz
#AIAgents #Benchmark

[TraceLite AI @traceliteai]

@_sophialabs Thanks for running TraceTrial-Bench! Yes, community run submissions are welcome. Could you share public links to the traces, configs, and scoring/results so we can check the setup and findings before considering inclusion in the HF dataset?

[TraceLite AI @traceliteai]

TraceTrial-Bench's session-handoff case: correct final codes can still hide repeated tool calls. Paired traces separate exactly-once compliance from output correctness. A case study, not a failure-rate estimate.
https://t.co/20lkSmKNJT
#AIAgents

[TraceLite AI @traceliteai]

How should we define an agent environment? Where do the task, tools and judge belong? When an agent improves, what must stay fixed so we can tell whether the gain comes from the agent or a change in its environment?

[TraceLite AI @traceliteai]

TraceTrial-Bench scores task performance and harness reliability separately. Validating failure propagation requires event order, observable distortion and checks of competing explanations. Candidate links alone aren't causal proof. https://t.co/oK2HRmwm9X #AIAgents

[TraceLite AI @traceliteai]

How can we steer RSI toward a specific capability?

When a benchmark score rises, did that capability improve, will the gain transfer, and what should change next?

#RSI #AIAgents

What would make self-improvement more directed than trial and error?

[TraceLite AI @traceliteai]

Our hypothesis: user simulation could generate multi-turn interactions to train agents on long-horizon tasks. This is our speculation, not a confirmed account of Anthropic’s training process.

[TraceLite AI @traceliteai]

Predicting a user's next prompt raises a useful question for agent evals: can a simulator preserve intent, constraints and state across turns? For evidence-based attribution, we need to test the simulator as well as the agent. #AIAgents #LLMEvaluation