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ARXIV:2605.10870 · AGENTS · SUBMITTED 12 MAY · 20:16 UTC · FRESHNESS FRESH
ARXIV:2605.10870AGENTSSUBMITTED 12 MAY · 20:16 UTCFRESHNESS FRESHMingxi Zou · Zhihan Guo · Langzhang Liang · Zhuo Wang · Qifan Wang · Qingsong Wen · +3 at arXiv
A rate-distortion framework for agent memory that prioritizes decision quality over descriptive accuracy, offering an exact forgetting boundary and an optimal tradeoff frontier.
Opportunity summary
Pain A rate-distortion framework for agent memory that prioritizes decision quality over descriptive accuracy, offering an exact forgetting boundary and an optimal tradeoff frontier.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
A rate-distortion framework for agent memory that prioritizes decision quality over descriptive accuracy, offering an exact forgetting boundary and an optimal tradeoff frontier. For an agent, however, memory is valuable not because it faithfully…
Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, salience, or summary quality. For an agent, however, memory is valuable not…
ScienceToStartup currently rates this 4.0/10 on the public viability pass. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must…
Agents moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
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Score4.0Analysis summary
A rate-distortion framework for agent memory that prioritizes decision quality over descriptive accuracy, offering an exact forgetting boundary and an optimal tradeoff frontier.
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Paper Pack
10.48550/arXiv.2605.10870A rate-distortion framework for agent memory that prioritizes decision quality over descriptive accuracy, offering an exact forgetting boundary and an optimal tradeoff frontier.
Abstract
Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, salience, or summary quality. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must remain separated under a fixed budget to support good decisions. We cast this as a decision-centric rate-distortion problem, measuring memory quality by the loss in achievable decision quality induced by compression. This yields an exact forgetting boundary for what can be safely forgotten, and a memory-distortion frontier characterizing the optimal tradeoff between memory budget and decision quality. Motivated by this decision-centric view of memory, we propose DeMem, an online memory learner that refines its partition only when data certify that a shared state would induce decision conflict, and prove near-minimax regret guarantees. On both controlled synthetic diagnostics and long-horizon conversational benchmarks, DeMem yields consistent gains under the same runtime budget, supporting the principle that memory should preserve the distinctions that matter for decisions, not descriptions.
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PROBLEM
A rate-distortion framework for agent memory that prioritizes decision quality over descriptive accuracy, offering an exact forgetting boundary and an optimal tradeoff frontier. For an agent, however, memory is valuable not because it faithfully describes the past, but because i...
METHOD
Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, salience, or summary quality. For an agent, however, memory is valuable not because it faithfully descr...
RESULT
ScienceToStartup currently rates this 4.0/10 on the public viability pass. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must remain separated under a fixed budget to suppo...
WHY NOW
Agents moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A rate-distortion framework for agent memory that prioritizes decision quality over descriptive accuracy, offering an exact forgetting boundary and an optimal tradeoff frontier. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must remain separated under a fixed budget to support good decisions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Long-horizon language agents must operate under limited runtime memory, yet existing memory mechanisms often organize experience around descriptive criteria such as relevance, salience, or summary quality. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must remain separated under a fixed budget to support good decisions.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 4.0/10 on the public viability pass. For an agent, however, memory is valuable not because it faithfully describes the past, but because it preserves the distinctions between histories that must remain separated under a fixed budget to support good decisions. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Agents moved forward this cycle; last verified May 2026. Public score 4.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A rate-distortion framework for agent memory that prioritizes decision quality over descriptive accuracy, offering an exact forgetting boundary and an optimal tradeoff frontier.
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