Human Feedback Types
strongCritique Edit
Directly usable for protocol triage.
"Memory enables language agents to carry useful distinctions from past experience into future decisions."
HFEPX · Eval paper review
The Value of Memory in Language Agents
Zehao Lin, Erenkai Zhu, Helan Hu, Xixuan Hao +2 more
Published
Oct 22, 2026
Citations
0
Trust level
Moderate
Usefulness score
57/100 (Medium)
Extraction confidence
70% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Not reported
Signals refreshed
Oct 22, 2026
Should you rely on this paper?
This paper has useful evaluation signal, but protocol completeness is partial; pair it with related papers before deciding implementation strategy.
Use this for comparison and orientation, not as your only source.
Low-signal caution for protocol decisions
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
- The abstract does not clearly name benchmarks or metrics.
Best use
Secondary protocol comparison source
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The abstract does not clearly name benchmarks or metrics.
Human feedback signal
Detected
From extracted signals
Evaluation signal
Detected
Eval setup described
Usefulness for eval research
57/100
Moderate-confidence candidate
Useful as a secondary reference; validate protocol details against neighboring papers.
If you are doing eval pipeline work, start here
Abstract
Memory enables language agents to carry useful distinctions from past experience into future decisions. Its value depends on which distinctions survive, whether a bounded agent can use them, and what can be recovered or revised as demands evolve. We develop a decision-theoretic framework that follows this value from raw history through persistent state, operational use, and costly recovery and revision. Original and retained value are expressed in units of avoided task loss; risk-cost frontiers describe attainable recovery and preservation across uses. Unknown demand yields value profiles, while interactive feedback changes the distribution of future work. A cost-aware simulation condition and five minimal counterexamples expose the roles of information timing, retrieval access, component interaction, feedback and demand uncertainty. In particular, a pointer with zero mutual information about the answer can still reduce the cost of exact recovery. Diagnostics on LoCoMo, a two-project update task and longitudinal cases exhibit failures of preservation, use and measurement. At fixed retrieved evidence, a saved ledger changes subsequent answers; at fixed agent outputs, an evaluator revision changes scores. The framework connects these distinctions to operational risk bounds and testable principles for allocating memory, preserving recovery options and revising evidence dependencies.
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.
Evaluation Modes
strongSimulation Env
Includes extracted eval setup.
"Memory enables language agents to carry useful distinctions from past experience into future decisions."
Quality Controls
missingNot reported
No explicit QC controls found.
"Memory enables language agents to carry useful distinctions from past experience into future decisions."
Benchmarks / Datasets
missingNot extracted
No benchmark anchors detected.
"Memory enables language agents to carry useful distinctions from past experience into future decisions."
Reported Metrics
missingNot extracted
No metric anchors detected.
"Memory enables language agents to carry useful distinctions from past experience into future decisions."
Benchmarks and datasets
No benchmark or dataset names were extracted from the available abstract.
Reported metrics
No metric terms were extracted from the available abstract.
Human feedback details
- Uses human feedback
- Yes
- Feedback types
- Critique Edit
- Rater population
- Not reported
- Expertise required
- General
Evaluation details
- Evaluation modes
- Simulation Env
- Agentic eval
- None
- Quality controls
- Not reported
- Evidence quality
- Moderate
- Use this page as
- Secondary protocol comparison source
Research brief
Metadata summary
Memory enables language agents to carry useful distinctions from past experience into future decisions.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Key takeaways
- Memory enables language agents to carry useful distinctions from past experience into future decisions.
- Its value depends on which distinctions survive, whether a bounded agent can use them, and what can be recovered or revised as demands evolve.
- We develop a decision-theoretic framework that follows this value from raw history through persistent state, operational use, and costly recovery and revision.
Researcher actions
- Compare this paper against nearby papers in the same arXiv category before using it for protocol decisions.
- Validate inferred eval signals (Simulation environment) 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
Contribution summary
- Memory enables language agents to carry useful distinctions from past experience into future decisions.
- Its value depends on which distinctions survive, whether a bounded agent can use them, and what can be recovered or revised as demands evolve.
- We develop a decision-theoretic framework that follows this value from raw history through persistent state, operational use, and costly recovery and revision.
Why it matters for eval
- Memory enables language agents to carry useful distinctions from past experience into future decisions.
- Its value depends on which distinctions survive, whether a bounded agent can use them, and what can be recovered or revised as demands evolve.
Researcher checklist
-
Human feedback protocol is explicit
Detected: Critique Edit
-
Evaluation mode is explicit
Detected: Simulation Env
-
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
-
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
-
Metric reporting is present
No metric terms extracted.