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Reclaim Evaluation: A Lossy Memory Is Worse Than an Empty One

Alex Kwon · Jun 24, 2026 · Citations: 0

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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 exact study setup in the full paper before operational use.

Evidence quality

Low

Derived from extracted protocol signals and abstract evidence.

Abstract

A language model's memory can be worse than no memory at all. A memory that keeps a wrong conclusion but drops the work behind it makes the model emit the stale value as a confident answer, where an empty memory would make it abstain; we call this brittle memory. We measure it with reclaim evaluation: compress a drifted interaction at a fixed budget, then test whether a correction recovers the known answer, scored against ground truth with no judge. Correctability is bottlenecked not by capability but by whether the answer-determining source survives compression, so an 8B model and a frontier one wall in the same place. Across eight models a lossy memory is never better than an empty one, and strictly worse on those disposed to answer rather than abstain. A one-line source-first policy, keep the recomputable source and drop the re-derivable conclusion, restores correctability at equal budget where the answer-determining source is compact and identifiable; a length-matched control rules out added text, and a deployable one-prompt form reclaims 0.49-0.88, rising toward the oracle's 1.00 when a frontier model writes the note. The failure compounds through a memory loop and replicates on three deployed memory systems and on real dialogue (MultiWOZ), with a located boundary past which the fix fails silently unless the note records its completeness. This is a controlled study of a mechanism: judge-free exact scoring, matched-budget controls, and validators built to come out false; we release the harness, the paired memory conditions, and these validators.

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.
  • The abstract does not clearly describe the evaluation setup.
Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub

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

Background context only.

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

Weak / implicit signal

Usefulness for eval research

Adjacent candidate

Extraction confidence 35%

If you are doing eval pipeline work, start here:

Human Eval Hub LLM-as-Judge Hub Pairwise Preference Hub Tool-Use Eval Hub

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.

"A language model's memory can be worse than no memory at all."

Evaluation Modes

missing

None explicit

Validate eval design from full paper text.

"A language model's memory can be worse than no memory at all."

Quality Controls

partial

Gold Questions

Calibration/adjudication style controls detected.

"A language model's memory can be worse than no memory at all."

Benchmarks / Datasets

partial

DROP

Useful for quick benchmark comparison.

"A memory that keeps a wrong conclusion but drops the work behind it makes the model emit the stale value as a confident answer, where an empty memory would make it abstain; we call this brittle memory."

Reported Metrics

missing

Not extracted

No metric anchors detected.

"A language model's memory can be worse than no memory at all."

Human Feedback Details

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

Evaluation Details

  • Evaluation modes:
  • Agentic eval: None
  • Quality controls: Gold Questions
  • Evidence quality: Low
  • Use this page as: Background context only

Protocol And Measurement Signals

Benchmarks / Datasets

DROP

Reported Metrics

No metric terms were extracted from the available abstract.

Research Brief

Metadata summary

A language model's memory can be worse than no memory at all.

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

Key Takeaways

  • A language model's memory can be worse than no memory at all.
  • A memory that keeps a wrong conclusion but drops the work behind it makes the model emit the stale value as a confident answer, where an empty memory would make it abstain; we call this brittle memory.
  • We measure it with reclaim evaluation: compress a drifted interaction at a fixed budget, then test whether a correction recovers the known answer, scored against ground truth with no judge.

Researcher Actions

  • Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
  • Check the full text for explicit evaluation design choices (raters, protocol, and metrics).
  • 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

  • We measure it with reclaim evaluation: compress a drifted interaction at a fixed budget, then test whether a correction recovers the known answer, scored against ground truth with no judge.
  • This is a controlled study of a mechanism: judge-free exact scoring, matched-budget controls, and validators built to come out false; we release the harness, the paired memory conditions, and these validators.

Why It Matters For Eval

  • We measure it with reclaim evaluation: compress a drifted interaction at a fixed budget, then test whether a correction recovers the known answer, scored against ground truth with no judge.
  • This is a controlled study of a mechanism: judge-free exact scoring, matched-budget controls, and validators built to come out false; we release the harness, the paired memory conditions, and these validators.

Researcher Checklist

  • Gap: Human feedback protocol is explicit

    No explicit human feedback protocol detected.

  • Gap: Evaluation mode is explicit

    No clear evaluation mode extracted.

  • Pass: Quality control reporting appears

    Detected: Gold Questions

  • Pass: Benchmark or dataset anchors are present

    Detected: DROP

  • Gap: Metric reporting is present

    No metric terms extracted.

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