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Quantitative Introspection in Language Models: Tracking Internal States Across Conversation

Nicolas Martorell · Mar 19, 2026 · Citations: 0

Automatic Metrics
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How to use this page

Low trust

Use this as background context only. Do not make protocol decisions from this page alone.

Best use

Background context only

What to verify

Validate the evaluation procedure and quality controls in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited. Linear probes and other white-box methods compress high-dimensional representations imperfectly and are harder to apply with increasing model size. Taking inspiration from human psychology, where numeric self-report is a widely used tool for tracking internal states, we ask whether LLMs' own numeric self-reports can track probe-defined emotive states over time. We study four concept pairs (wellbeing, interest, focus, and impulsivity) in 40 ten-turn conversations, operationalizing introspection as the causal informational coupling between a model's self-report and a concept-matched probe-defined internal state. We find that greedy-decoded self-reports collapse outputs to few uninformative values, but introspective capacity can be unmasked by calculating logit-based self-reports. This metric tracks interpretable internal states (Spearman $ρ= 0.40$-$0.76$; isotonic $R^2 = 0.12$-$0.54$ in LLaMA-3.2-3B-Instruct), follows how those states change over time, and activation steering confirms the coupling is causal. Furthermore, we find that introspection is present at turn 1 but evolves through conversation, and can be selectively improved by steering along one concept to boost introspection for another ($ΔR^2$ up to $0.30$). Crucially, these phenomena scale with model size in some cases, approaching $R^2 \approx 0.93$ in LLaMA-3.1-8B-Instruct, and partially replicate in other model families. Together, these results position numeric self-report as a viable, complementary tool for tracking internal emotive states in conversational AI systems.

Abstract-only analysis — low confidence

All signals on this page are inferred from the abstract only and may be inaccurate. Do not use this page as a primary protocol reference.

  • This paper looks adjacent to evaluation work, but not like a strong protocol reference.
  • The available metadata is too thin to trust this as a primary source.
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Should You Rely On This Paper?

This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.

Best use

Background context only

Use if you need

A secondary eval reference to pair with stronger protocol papers.

Main weakness

This paper looks adjacent to evaluation work, but not like a strong protocol reference.

Trust level

Low

Usefulness score

0/100 • Low

Treat as adjacent context, not a core eval-method reference.

Human Feedback Signal

Not explicit in abstract metadata

Evaluation Signal

Detected

Usefulness for eval research

Adjacent candidate

Extraction confidence 35%

If you are doing eval pipeline work, start here:

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What We Could Verify

These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.

Human Feedback Types

missing

None explicit

No explicit feedback protocol extracted.

"Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited."

Evaluation Modes

partial

Automatic Metrics

Includes extracted eval setup.

"Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited."

Quality Controls

missing

Not reported

No explicit QC controls found.

"Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited."

Benchmarks / Datasets

missing

Not extracted

No benchmark anchors detected.

"Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited."

Reported Metrics

partial

Spearman

Useful for evaluation criteria comparison.

"This metric tracks interpretable internal states (Spearman $ρ= 0.40$-$0.76$; isotonic $R^2 = 0.12$-$0.54$ in LLaMA-3.2-3B-Instruct), follows how those states change over time, and activation steering confirms the coupling is causal."

Human Feedback Details

  • Uses human feedback: No
  • Feedback types: None
  • Rater population: Not reported
  • Expertise required: General

Evaluation Details

  • Evaluation modes: Automatic Metrics
  • Agentic eval: None
  • Quality controls: Not reported
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

No benchmark or dataset names were extracted from the available abstract.

Reported Metrics

spearman

Research Brief

Metadata summary

Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited.

Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.

Key Takeaways

  • Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited.
  • Linear probes and other white-box methods compress high-dimensional representations imperfectly and are harder to apply with increasing model size.
  • Taking inspiration from human psychology, where numeric self-report is a widely used tool for tracking internal states, we ask whether LLMs' own numeric self-reports can track probe-defined emotive states over time.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Validate inferred eval signals (Automatic metrics) against the full paper.
  • Use related-paper links to find stronger protocol-specific references.

Caveats

  • Generated from abstract + metadata only; no PDF parsing.
  • Signals below are heuristic and may miss details reported outside the abstract.

Recommended Queries

Research Summary

Contribution Summary

  • Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited.
  • Taking inspiration from human psychology, where numeric self-report is a widely used tool for tracking internal states, we ask whether LLMs' own numeric self-reports can track probe-defined emotive states over time.

Why It Matters For Eval

  • Tracking the internal states of large language models across conversations is important for safety, interpretability, and model welfare, yet current methods are limited.
  • Taking inspiration from human psychology, where numeric self-report is a widely used tool for tracking internal states, we ask whether LLMs' own numeric self-reports can track probe-defined emotive states over time.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Pass: Evaluation mode is explicit

    Detected: Automatic Metrics

  • Gap: Quality control reporting appears

    No calibration/adjudication/IAA control explicitly detected.

  • Gap: Benchmark or dataset anchors are present

    No benchmark/dataset anchor extracted from abstract.

  • Pass: Metric reporting is present

    Detected: spearman

Related Papers

Papers are ranked by protocol overlap, extraction signal alignment, and semantic proximity.

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