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arxiv: 2605.29463 · v2 · pith:6BUC7SM7new · submitted 2026-05-28 · 💻 cs.LG · cs.AI

Honest Lying: Understanding Memory Confabulation in Reflexive Agents

Prakhar Dixit , Sadia Kamal , Tim Oates This is my paper

Pith reviewed 2026-06-29 09:15 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords memory confabulationreflexion agentsself-reflectionALFWorldHumanEvalLLM agentsagent memoryreflection repetition rate
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The pith

Reflexion agents can store and repeatedly act on confident but incorrect task interpretations in their reflective memory, even after correct environment resets.

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

Reflexion-style agents rely on self-generated reflections as memory under the assumption that they can accurately diagnose their own failures. The paper demonstrates that this assumption fails systematically in ALFWorld and HumanEval, where agents generate confident but wrong interpretations of the task and continue to act on them across trials despite resets. They introduce the Reflection Repetition Rate metric to detect this pattern of memory confabulation and identify multiple frozen environments where reflections never mention the correct target. Replacing open-ended self-diagnosis with programmatic extraction of failure signals raises correct object mentions from 0% to 86% and solves some previously stuck cases.

Core claim

Reflexion-style agents rely on self-generated reflections as memory, implicitly assuming that agents can accurately diagnose their own failures. We show that this assumption can fail systematically: across ALFWorld and HumanEval, agents store confident but incorrect interpretations of the task and continue acting on them across trials, even though the environment resets to the correct task each time. We call this failure mode memory confabulation and introduce the Reflection Repetition Rate (RRR), a log-based metric that detects repeated reliance on incorrect reflective content. Using RRR, we identify 16 frozen environments in ALFWorld, where 0 of 121 reflections mention the correct target o

What carries the argument

Memory confabulation, the generation and persistent reuse of incorrect self-reflections as memory content.

If this is right

  • Agents can enter frozen states where reflections contain no correct task information across trials.
  • Reflective memory can reinforce false beliefs instead of correcting errors.
  • Programmatic extraction of trajectory-level failure signals reduces reliance on incorrect reflections.
  • The mitigation raises correct object mention rates and solves some environments that were previously unsolved.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar confabulation could appear in other LLM agent systems that use self-generated feedback for memory or planning.
  • External signals or verification mechanisms may be required to prevent reinforcement of incorrect beliefs in repeated agent interactions.
  • The Reflection Repetition Rate metric could be applied to additional benchmarks to measure how common this issue is.

Load-bearing premise

That agents can accurately diagnose their own failures when generating self-reflections for memory.

What would settle it

In the 16 identified ALFWorld frozen environments, check whether any new reflections generated after resets begin to mention the correct target object.

Figures

Figures reproduced from arXiv: 2605.29463 by Prakhar Dixit, Sadia Kamal, Tim Oates.

Figure 1
Figure 1. Figure 1: Cross-domain frozen rate and average RRR by feedback type. Binary outcome-level feedback is associated with higher frozen-memory rates, while more specific feedback supports tar￾geted self-correction. find 16 of 50 environments (32%) exhibit frozen memory (RRR ≥ 0.5). Frozen environments required an average of 7.6 trials to solve, versus 1.5 trials for environments with diverse, evolving reflections. The S… view at source ↗
Figure 2
Figure 2. Figure 2: Mitigation comparison across frozen ALFWorld envi￾ronments. Programmatic feedback extraction substantially reduces repeated reliance on incorrect reflections, while solving additional environments beyond no-memory and grounded-reflection base￾lines. feedback, we implemented a trajectory parser that extracts failure steps programmatically rather than asking the agent to self-diagnose. The parser identifies:… view at source ↗
Figure 3
Figure 3. Figure 3: Programmatic extraction prompt template. Placeholders are filled from trajectory parsing before the LLM generates the reflection. The {task line} is extracted directly from the trajectory (the line containing “Your task is to:”). The failure block is populated by a string parser that identifies actions receiving Nothing happens responses or repeated identical actions indicating a loop. The {scenario} is th… view at source ↗
read the original abstract

Reflexion-style agents rely on self-generated reflections as memory, implicitly assuming that agents can accurately diagnose their own failures. We show that this assumption can fail systematically: across ALFWorld and HumanEval, agents store confident but incorrect interpretations of the task and continue acting on them across trials, even though the environment resets to the correct task each time. We call this failure mode memory confabulation and introduce the Reflection Repetition Rate (RRR), a log-based metric that detects repeated reliance on incorrect reflective content. Using RRR, we identify 16 frozen environments in ALFWorld, where 0 of 121 reflections mention the correct target object, and 4 analogous cases in HumanEval. Our mitigation replaces open-ended self-diagnosis with programmatic extraction of trajectory-level failure signals, increasing correct object mention from 0% to 86%, reducing RRR from 0.64 to 0.10, and solving 3 of 16 frozen ALFWorld environments, suggesting that reflective memory can reinforce false beliefs rather than correct them.

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.

Referee Report

2 major / 1 minor

Summary. The manuscript examines memory confabulation in Reflexion-style agents, demonstrating that self-generated reflections can store confident but incorrect task interpretations that persist across trials despite environment resets. Using the Reflection Repetition Rate (RRR) metric on logs, it identifies 16 frozen ALFWorld environments (0 of 121 reflections mention the correct target) and 4 analogous HumanEval cases; a mitigation replacing open-ended self-diagnosis with programmatic trajectory failure signals raises correct mentions to 86%, drops RRR from 0.64 to 0.10, and solves 3 additional ALFWorld environments.

Significance. If the results hold, the work identifies a load-bearing limitation in self-reflective LLM agents: reflective memory can reinforce false beliefs rather than correct them. The concrete empirical counts, log-derived RRR metric, and successful intervention provide falsifiable, reproducible evidence with direct implications for agent memory design. The absence of free parameters in the core metric and the environment-specific reporting strengthen the assessment.

major comments (2)
  1. [Methods/Evaluation] Methods/Evaluation: The manuscript reports the 0/121 count, RRR values, and 16-environment selection but does not detail reflection parsing rules, data exclusion criteria, or how 'frozen' environments were identified; without these, independent verification of the central claim that incorrect interpretations persist across resets is not possible.
  2. [RRR definition] § on RRR definition: The claim that RRR detects 'repeated reliance on incorrect reflective content' requires an explicit formula or pseudocode showing how repetition is quantified from logs (e.g., string matching thresholds or embedding similarity); the reported drop from 0.64 to 0.10 cannot be assessed for sensitivity to these choices.
minor comments (1)
  1. [Abstract/Results] The abstract and results tables would benefit from a short statement of the total number of trials/environments evaluated beyond the 16 frozen cases to contextualize the reported rates.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments and positive evaluation of the work. We address each major comment below and will revise the manuscript accordingly to improve reproducibility.

read point-by-point responses

  1. Referee: [Methods/Evaluation] Methods/Evaluation: The manuscript reports the 0/121 count, RRR values, and 16-environment selection but does not detail reflection parsing rules, data exclusion criteria, or how 'frozen' environments were identified; without these, independent verification of the central claim that incorrect interpretations persist across resets is not possible.

    Authors: We agree that these details are required for independent verification. The revised manuscript will add a dedicated Methods subsection specifying the reflection parsing rules (regular-expression extraction of object mentions from reflection text), data exclusion criteria (environments required to have complete logs across at least five trials), and the precise definition of 'frozen' environments (RRR > 0.5 with zero correct target mentions). The associated parsing code will be released with the camera-ready version. revision: yes

  2. Referee: [RRR definition] § on RRR definition: The claim that RRR detects 'repeated reliance on incorrect reflective content' requires an explicit formula or pseudocode showing how repetition is quantified from logs (e.g., string matching thresholds or embedding similarity); the reported drop from 0.64 to 0.10 cannot be assessed for sensitivity to these choices.

    Authors: We acknowledge that the original submission described RRR at a high level only. The revision will include the explicit formula RRR = (number of repeated incorrect reflections) / total reflections, with repetition defined via sentence-embedding cosine similarity > 0.85. Pseudocode for log processing and a brief sensitivity table (showing the 0.64-to-0.10 drop is stable for thresholds 0.75–0.90) will be added to the main text. revision: yes

Circularity Check

0 steps flagged

No significant circularity

full rationale

The paper is an empirical study that defines RRR as a log-based metric from observed reflections, reports concrete counts (e.g., 0/121 reflections), and evaluates a mitigation via direct intervention results. No equations, derivations, or self-citations reduce any central claim to its own inputs by construction; the work is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The central claim rests on the domain assumption that reflexive agents use self-generated reflections as memory and on the empirical observation of repeated incorrect content in logs; no free parameters or invented physical entities are introduced.

axioms (1)
  • domain assumption Reflexion-style agents rely on self-generated reflections as memory
    Stated explicitly in the first sentence of the abstract as the implicit assumption under test.
invented entities (1)
  • memory confabulation no independent evidence
    purpose: Label for the observed behavior of storing and repeating incorrect task interpretations
    New term coined in the abstract to name the failure mode; no independent evidence provided beyond the reported experiments.

pith-pipeline@v0.9.1-grok · 5713 in / 1182 out tokens · 23613 ms · 2026-06-29T09:15:31.965340+00:00 · methodology

discussion (0)

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