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[token-optimization] — Cache Retrieval Traces as Reusable "Experience Edges"
Paper: VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents Authors: Peiyuan Gao, Gaoyuan Zhang, Haojie Qin Published: 2026-09-10 Effort: medium Rationale: VikingRAG materializes successful multi-round retrieval traces as "experience edges" that are reused for similar queries, avoiding repeated multi-round exploration and cutting token overhead. gh-aw sub-agents (e.g. explore, research) that repeatedly re-investigate similar codebase/document queries across workflow runs could cache successful tool-call sequences in cache-memory or repo-memory, keyed by query similarity, and only escalate to full multi-round exploration when no cached trace covers the new query — directly reducing token consumption for repeated research-style sub-agent tasks.
[other] — Freshness-Aware Refresh Scheduling for Cache/Repo Memory
Paper: ChurnBench: A Drift-Aware Benchmark Demonstrating That Refresh Scheduling, Not Cache Age, Governs Staleness in Agentic AI Authors: Vivek Kumar Singh, Preeti Priyam Published: 2026-09-10 Effort: medium Rationale: ChurnBench shows that whether a cached answer is stale depends on refresh scheduling relative to underlying data churn, not raw cache age, and it separately labels "freshness errors" (correct-when-retrieved, wrong-when-evaluated) from reasoning errors. gh-aw's cache-memory and repo-memory currently persist data with a simple "last write wins" policy; adopting churn-aware refresh scheduling (and tagging outputs derived from memory as freshness-sensitive) would let workflows detect and flag stale memory-derived answers instead of silently trusting old cached data.
[other] — Principled Failure Diagnosis Before Harness/Engine Revision
Paper: Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents Authors: Ruiqing Yue, Yu Cui, Zhuoyu Sun Published: 2026-09-10 Effort: low Rationale: Ecdysis identifies that harness-evolution methods waste time and overfit because they lack principled diagnosis of whether an observed failure is model-specific or a systematic harness defect before revising the harness. gh-aw's custom engine implementation and error-pattern-safety guidance could adopt a similar diagnosis step — classifying tool-execution failures or error-pattern mismatches as model-specific vs. systematic before changing engine behavior, retry rules, or error-pattern regexes — reducing wasted iteration from misattributed fixes.
Investigate: cache reusable retrieval/experience traces for repeated sub-agent research tasks (effort: medium)
Investigate: freshness-aware refresh scheduling for cache-memory/repo-memory (effort: medium)
Investigate: principled model-specific vs. systematic failure diagnosis before harness/engine revision (effort: low)
Quick-Win Agentic Prompts
Paste one of these as a new issue or comment to kick off implementation with @copilot
(one prompt per opportunity, in the same order as above):
@copilot Implement: materialize successful multi-round retrieval/tool-call traces as reusable "experience edges" keyed by query similarity so repeated sub-agent research tasks skip redundant multi-round exploration, escalating adaptively only when no cached trace covers the new query in gh-aw's token-optimization component. Rationale: VikingRAG materializes successful multi-round retrieval traces as "experience edges" that are reused for similar queries, avoiding repeated multi-round exploration and cutting token overhead. gh-aw sub-agents (e.g. explore, research) that repeatedly re-investigate similar codebase/document queries across workflow runs could cache successful tool-call sequences in cache-memory or repo-memory, keyed by query similarity, and only escalate to full multi-round exploration when no cached trace covers the new query — directly reducing token consumption for repeated research-style sub-agent tasks. Source: VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents.
@copilot Implement: add freshness-aware refresh scheduling to cache-memory/repo-memory keyed on source update-rate rather than fixed cache age, and distinguish freshness errors (stale-at-evaluation) from reasoning errors when validating memory-backed outputs in gh-aw's other (memory) component. Rationale: ChurnBench shows that whether a cached answer is stale depends on refresh scheduling relative to underlying data churn, not raw cache age, and it separately labels "freshness errors" (correct-when-retrieved, wrong-when-evaluated) from reasoning errors. gh-aw's cache-memory and repo-memory currently persist data with a simple "last write wins" policy; adopting churn-aware refresh scheduling (and tagging outputs derived from memory as freshness-sensitive) would let workflows detect and flag stale memory-derived answers instead of silently trusting old cached data. Source: ChurnBench: A Drift-Aware Benchmark Demonstrating That Refresh Scheduling, Not Cache Age, Governs Staleness in Agentic AI.
@copilot Implement: classify tool-execution/error-pattern failures as model-specific vs. systematic before revising engine behavior, retry rules, or error-pattern regexes in gh-aw's other (custom-engine/error-recovery) component. Rationale: Ecdysis identifies that harness-evolution methods waste time and overfit because they lack principled diagnosis of whether an observed failure is model-specific or a systematic harness defect before revising the harness. gh-aw's custom engine implementation and error-pattern-safety guidance could adopt a similar diagnosis step — classifying tool-execution failures or error-pattern mismatches as model-specific vs. systematic before changing engine behavior, retry rules, or error-pattern regexes — reducing wasted iteration from misattributed fixes. Source: Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents.
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Summary
25 papers screened, 10 relevant, 3 actionable opportunities identified.
Actionable Opportunities
[token-optimization] — Cache Retrieval Traces as Reusable "Experience Edges"
Paper: VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documents
Authors: Peiyuan Gao, Gaoyuan Zhang, Haojie Qin
Published: 2026-09-10
Effort: medium
Rationale: VikingRAG materializes successful multi-round retrieval traces as "experience edges" that are reused for similar queries, avoiding repeated multi-round exploration and cutting token overhead. gh-aw sub-agents (e.g. explore, research) that repeatedly re-investigate similar codebase/document queries across workflow runs could cache successful tool-call sequences in cache-memory or repo-memory, keyed by query similarity, and only escalate to full multi-round exploration when no cached trace covers the new query — directly reducing token consumption for repeated research-style sub-agent tasks.
[other] — Freshness-Aware Refresh Scheduling for Cache/Repo Memory
Paper: ChurnBench: A Drift-Aware Benchmark Demonstrating That Refresh Scheduling, Not Cache Age, Governs Staleness in Agentic AI
Authors: Vivek Kumar Singh, Preeti Priyam
Published: 2026-09-10
Effort: medium
Rationale: ChurnBench shows that whether a cached answer is stale depends on refresh scheduling relative to underlying data churn, not raw cache age, and it separately labels "freshness errors" (correct-when-retrieved, wrong-when-evaluated) from reasoning errors. gh-aw's cache-memory and repo-memory currently persist data with a simple "last write wins" policy; adopting churn-aware refresh scheduling (and tagging outputs derived from memory as freshness-sensitive) would let workflows detect and flag stale memory-derived answers instead of silently trusting old cached data.
[other] — Principled Failure Diagnosis Before Harness/Engine Revision
Paper: Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents
Authors: Ruiqing Yue, Yu Cui, Zhuoyu Sun
Published: 2026-09-10
Effort: low
Rationale: Ecdysis identifies that harness-evolution methods waste time and overfit because they lack principled diagnosis of whether an observed failure is model-specific or a systematic harness defect before revising the harness. gh-aw's custom engine implementation and error-pattern-safety guidance could adopt a similar diagnosis step — classifying tool-execution failures or error-pattern mismatches as model-specific vs. systematic before changing engine behavior, retry rules, or error-pattern regexes — reducing wasted iteration from misattributed fixes.
Papers Analyzed
Next Steps
Quick-Win Agentic Prompts
Paste one of these as a new issue or comment to kick off implementation with
@copilot(one prompt per opportunity, in the same order as above):
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