arxiv: 2604.19775 · v1 · submitted 2026-03-27 · 💻 cs.AI · cs.CL· cs.ET· cs.MA· cs.RO

Recognition: 2 theorem links

· Lean Theorem

From Actions to Understanding: Conformal Interpretability of Temporal Concepts in LLM Agents

Trilok Padhi , Ramneet Kaur , Krishiv Agarwal , Adam D. Cobb , Daniel Elenius , Manoj Acharya , Colin Samplawski , Alexander M. Berenbeim

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Nathaniel D. Bastian Susmit Jha Anirban Roy

Authors on Pith no claims yet

Pith reviewed 2026-05-14 22:29 UTC · model grok-4.3

classification 💻 cs.AI cs.CLcs.ETcs.MAcs.RO

keywords LLM agentsconformal interpretabilitytemporal conceptslinear probesagent steeringScienceWorldAlfWorldfailure detection

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The pith

Conformal prediction on step-wise rewards reveals linearly separable temporal concepts in LLM agent activations that align with task success.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper develops a method to interpret how LLM agents reason over time by labeling their internal states at each step. It combines reward modeling with conformal prediction to mark representations as successful or failing, then uses linear probes to find consistent directions corresponding to success, failure, or drift. Tests in two simulated environments show these directions are separable and match actual task performance. The approach also allows steering the model toward better outcomes. This matters because it could make black-box agents more transparent and controllable in interactive settings.

Core claim

The authors claim that by applying conformal prediction to step-wise reward signals, one can statistically annotate an LLM agent's hidden states at each timestep, after which linear probes trained on those annotations recover directions in activation space that represent temporal concepts such as success or reasoning drift, and that these directions are linearly separable in the tested environments while correlating with overall task success.

What carries the argument

Conformal interpretability framework combining step-wise reward modeling and conformal prediction to label activations, followed by linear probing to extract temporal concept directions.

Load-bearing premise

That conformal prediction applied to step-wise reward models provides accurate, low-noise labels for the agent's internal representations at each step.

What would settle it

Finding a new interactive task where the trained linear probes achieve no better than random accuracy in separating successful from failing steps, or where steering does not improve outcomes.

Figures

Figures reproduced from arXiv: 2604.19775 by Adam D. Cobb, Alexander M. Berenbeim, Anirban Roy, Colin Samplawski, Daniel Elenius, Krishiv Agarwal, Manoj Acharya, Nathaniel D. Bastian, Ramneet Kaur, Susmit Jha, Trilok Padhi.

**Figure 1.** Figure 1: A. We consider the problem of temporal interpretability of LLM agents trained to perform sequential tasks in complex environments. B. Proposed framework combines step-wise reward modeling with conformal labeling to distinguish success and failure at each timestep. Linear probes are trained on the model’s internal representations to test the hypothesis that step-wise notions of success and failure are linea… view at source ↗

**Figure 2.** Figure 2: Comparison of baseline (SFT Llama-2-7B) and steered LLM agents – Steering along learned success directions mitigates reasoning drift, reduces hallucinations, and improves task completion. gains, respectively, on the same test settings [PITH_FULL_IMAGE:figures/full_fig_p008_2.png] view at source ↗

**Figure 3.** Figure 3: Task examples from ScienceWorld and ALFWorld. [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗

read the original abstract

Large Language Models (LLMs) are increasingly deployed as autonomous agents capable of reasoning, planning, and acting within interactive environments. Despite their growing capability to perform multi-step reasoning and decision-making tasks, internal mechanisms guiding their sequential behavior remain opaque. This paper presents a framework for interpreting the temporal evolution of concepts in LLM agents through a step-wise conformal lens. We introduce the conformal interpretability framework for temporal tasks, which combines step-wise reward modeling with conformal prediction to statistically label model's internal representation at each step as successful or failing. Linear probes are then trained on these representations to identify directions of temporal concepts - latent directions in the model's activation space that correspond to consistent notions of success, failure or reasoning drift. Experimental results on two simulated interactive environments, namely ScienceWorld and AlfWorld, demonstrate that these temporal concepts are linearly separable, revealing interpretable structures aligned with task success. We further show preliminary results on improving an LLM agent's performance by leveraging the proposed framework for steering the identified successful directions inside the model. The proposed approach, thus, offers a principled method for early failure detection as well as intervention in LLM-based agents, paving the path towards trustworthy autonomous language models in complex interactive settings.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper pairs step-wise conformal labeling from reward models with linear probes to extract temporal success/failure directions in LLM agent activations on ScienceWorld and AlfWorld.

read the letter

The core contribution is a practical pipeline that labels each step in an LLM agent's trajectory using conformal prediction on top of a reward model, then fits linear probes to recover directions in the activation space tied to success, failure, or drift. They run this on ScienceWorld and AlfWorld and report that the concepts come out linearly separable, with some early steering experiments that nudge the model toward better trajectories by intervening on those directions. That combination and the concrete agent benchmarks are the parts worth noting; it is a straightforward extension of probing and conformal methods rather than a wholly new theoretical result. The experiments give a clear picture of what the method produces on standard interactive tasks, which is useful for anyone trying to inspect agent internals. The main soft spot is the labeling step itself. The framework assumes the conformal labels from the reward model track actual task progress reliably, but without reported calibration metrics, accuracy against simulator ground truth, or checks for bias in the reward model, it is possible the probes are mostly recovering reward-model artifacts instead of genuine internal representations. If the full paper includes those validation numbers and they hold up, the claim strengthens considerably; otherwise the directions remain hard to trust. This work is aimed at interpretability researchers who already work with LLM agents and want a concrete tool for temporal analysis. A reader looking for new benchmarks or formal guarantees will find less here. It is solid enough to send to peer review so that referees can check the label quality and probe details directly.

Referee Report

2 major / 2 minor

Summary. The paper introduces a conformal interpretability framework for LLM agents that combines step-wise reward modeling with conformal prediction to label internal activations at each timestep as successful or failing. Linear probes are then trained on these labeled representations to recover directions corresponding to temporal concepts such as success, failure, and reasoning drift. Experiments on ScienceWorld and AlfWorld are reported to show that these directions are linearly separable and aligned with task success, with preliminary results on using the directions for steering agent behavior toward improved performance.

Significance. If the labeling step is shown to be reliable, the framework would provide a statistically grounded method for interpreting and intervening in the sequential decision-making of LLM agents, with potential applications in early failure detection and trustworthy autonomy. The approach builds on standard linear probing and conformal techniques but applies them to temporal agent trajectories, which could be useful if the core separability result is robustly validated.

major comments (2)

[Abstract and §4] Abstract and §4 (Experiments): The central claim that temporal concepts are linearly separable rests on the step-wise reward model plus conformal prediction producing accurate success/failure labels. No quantitative validation (accuracy, calibration error, or agreement with simulator ground-truth outcomes) is reported for the labeling step on ScienceWorld or AlfWorld trajectories, leaving open the possibility that the recovered directions reflect reward-model artifacts rather than genuine internal representations.
[§3] §3 (Method): The description of how conformal prediction is applied to produce per-step labels does not specify the nonconformity score, the calibration set construction, or how the reward model is trained (e.g., on proxy signals versus verified environment outcomes). Without these details, it is impossible to assess whether the labeling procedure introduces systematic bias that would undermine the subsequent linear-probe results.

minor comments (2)

[Abstract] The abstract mentions 'preliminary results on improving an LLM agent's performance' via steering but provides no quantitative metrics, baselines, or statistical tests for the steering experiments.
[§4] Figure captions and experimental tables should include error bars, number of runs, and exact definitions of the linear-probe metrics used to claim separability.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments that identify key areas for strengthening the rigor and clarity of the manuscript. We address each major comment below and will revise the paper accordingly to incorporate the requested details and validations.

read point-by-point responses

Referee: [Abstract and §4] Abstract and §4 (Experiments): The central claim that temporal concepts are linearly separable rests on the step-wise reward model plus conformal prediction producing accurate success/failure labels. No quantitative validation (accuracy, calibration error, or agreement with simulator ground-truth outcomes) is reported for the labeling step on ScienceWorld or AlfWorld trajectories, leaving open the possibility that the recovered directions reflect reward-model artifacts rather than genuine internal representations.

Authors: We agree that explicit quantitative validation of the labeling step is essential to substantiate the central claims. In the revised manuscript, we will add a dedicated subsection in §4 reporting accuracy, calibration error, and agreement with simulator ground-truth outcomes for the conformal labels on both ScienceWorld and AlfWorld trajectories. These results will demonstrate that the labels are reliable and that the recovered directions reflect genuine internal representations rather than artifacts. revision: yes
Referee: [§3] §3 (Method): The description of how conformal prediction is applied to produce per-step labels does not specify the nonconformity score, the calibration set construction, or how the reward model is trained (e.g., on proxy signals versus verified environment outcomes). Without these details, it is impossible to assess whether the labeling procedure introduces systematic bias that would undermine the subsequent linear-probe results.

Authors: We acknowledge that the current description of the conformal prediction procedure lacks necessary technical details. The revised §3 will fully specify the nonconformity score (defined as the absolute prediction error from the reward model), the construction of the calibration set (from held-out trajectories with verified environment outcomes), and the reward model training process (including the use of proxy signals versus ground-truth). These additions will enable readers to evaluate potential biases and ensure the labeling procedure is transparent. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected; framework relies on standard external techniques

full rationale

The paper introduces a conformal interpretability framework that applies step-wise reward modeling and conformal prediction to label LLM activations, followed by training linear probes to detect temporal concepts. No equations, derivations, or self-citations are shown that reduce the claimed linear separability result to a fitted parameter or input by construction. The experimental demonstration on ScienceWorld and AlfWorld trajectories uses externally validated techniques (conformal prediction, linear probing) whose correctness does not depend on the target claim. The central result is presented as an empirical finding rather than a tautological renaming or self-referential definition, making the derivation self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract provides no explicit free parameters, axioms, or invented entities; the method description relies on standard conformal prediction and linear probing without introducing new postulated objects.

pith-pipeline@v0.9.0 · 5564 in / 1158 out tokens · 40841 ms · 2026-05-14T22:29:45.126090+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Foundation/AbsoluteFloorClosure.lean absolute_floor_iff_bare_distinguishability unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

We introduce the conformal interpretability framework for temporal tasks, which combines step-wise reward modeling with conformal prediction to statistically label model's internal representation at each step as successful or failing. Linear probes are then trained on these representations to identify directions of temporal concepts
IndisputableMonolith/Cost/FunctionalEquation.lean J_uniquely_calibrated_via_higher_derivative unclear

?

unclear
Relation between the paper passage and the cited Recognition theorem.

Tables 1-6 report probe accuracy/F1 on ScienceWorld and AlfWorld trajectories; steering via RepE contrastive directions yields 1.1% accuracy gain

What do these tags mean?

matches: The paper's claim is directly supported by a theorem in the formal canon.
supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
extends: The paper goes beyond the formal theorem; the theorem is a base layer rather than the whole result.
uses: The paper appears to rely on the theorem as machinery.
contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
unclear: Pith found a possible connection, but the passage is too broad, indirect, or ambiguous to say the theorem truly supports the claim.

Reference graph

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Testing Con- ductivity

Supplementary Material 7.1. Task Examples from ScienceWorld and Alf- World Figure 3 illustrates a representative task, “Testing Con- ductivity” in ScienceWorld, along with the corresponding action-observation trajectory, and Figure 3 shows an exam- ple trajectory of the agent-environment interaction on one of the household’s task of “cleaning a tomato and...

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