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Paper: CPU-ARC: Agentic Reasoning Caching for on-CPU Reasoning Reuse Authors: Peter Belcak, Greg Heinrich, Saurav Muralidharan Published: 2026-11-06 Effort: high Rationale: CPU-ARC separates expensive solution discovery from cheap application and reports 16–291× lower invocation cost while preserving frontier-model performance on its evaluated tasks. gh-aw could persist only procedures that pass workflow-owned verification, then reuse them through cache-memory for structurally equivalent tasks, reducing tokens and latency without treating unverified model output as executable truth.
Paper: When Instruction Files Go Stale: Documentation Debt and Hidden Cost in Coding Agents Authors: Tural Mehtiyev, Biruk Tadesse, Lawrence Arkoh Published: 2026-11-06 Effort: medium Rationale: The paper identifies stale AGENTS.md- and CLAUDE.md-style guidance as recurring documentation debt whose cost is paid in every later coding-agent session but can be hidden by task-success-only evaluation. gh-aw can add a preflight or scheduled audit that cross-checks instruction references against repository state and uses session traces to surface repeated recovery work, reducing wasted tokens and misleading agent behavior.
Sub-Agents — Parallel Team Orchestration
Paper: Test-Time Compute Scales Better with Parallel Agent Teams Authors: Junlin Wang, Haozhe Chen, Ang Li Published: 2026-11-06 Effort: medium Rationale: The paper finds that many small collaborating teams with verifier-based selection outperform both independent best-of-N agents and one large team on long-horizon tasks. gh-aw can expose team size, team count, and grader selection as declarative workflow settings, enabling better test-time scaling without requiring a stronger engine or an unbounded single swarm.
Investigate: Add verifier-gated reasoning caches that compile successful, repeatable agent procedures into reusable CPU-executable artifacts keyed by task signature, so later gh-aw runs can apply the procedure without repeating full-model reasoning. (effort: high)
Investigate: Add an instruction-freshness audit that validates repository guidance files against the current tree and command surface, then flags stale paths, commands, and assumptions before agent sessions consume them. (effort: medium)
Investigate: Add a parallel-team orchestration mode that launches several small, role-diverse sub-agent teams under a shared compute budget and selects the best team result through a configured grader. (effort: medium)
Quick-Win Agentic Prompts
@copilot Implement: Add verifier-gated reasoning caches that compile successful, repeatable agent procedures into reusable CPU-executable artifacts keyed by task signature, so later gh-aw runs can apply the procedure without repeating full-model reasoning. in gh-aw's cache-memory component. Rationale: CPU-ARC separates expensive solution discovery from cheap application and reports 16–291× lower invocation cost while preserving frontier-model performance on its evaluated tasks. gh-aw could persist only procedures that pass workflow-owned verification, then reuse them through cache-memory for structurally equivalent tasks, reducing tokens and latency without treating unverified model output as executable truth. Source: CPU-ARC: Agentic Reasoning Caching for on-CPU Reasoning Reuse.
@copilot Implement: Add an instruction-freshness audit that validates repository guidance files against the current tree and command surface, then flags stale paths, commands, and assumptions before agent sessions consume them. in gh-aw's developer-experience component. Rationale: The paper identifies stale AGENTS.md- and CLAUDE.md-style guidance as recurring documentation debt whose cost is paid in every later coding-agent session but can be hidden by task-success-only evaluation. gh-aw can add a preflight or scheduled audit that cross-checks instruction references against repository state and uses session traces to surface repeated recovery work, reducing wasted tokens and misleading agent behavior. Source: When Instruction Files Go Stale: Documentation Debt and Hidden Cost in Coding Agents.
@copilot Implement: Add a parallel-team orchestration mode that launches several small, role-diverse sub-agent teams under a shared compute budget and selects the best team result through a configured grader. in gh-aw's sub-agents component. Rationale: The paper finds that many small collaborating teams with verifier-based selection outperform both independent best-of-N agents and one large team on long-horizon tasks. gh-aw can expose team size, team count, and grader selection as declarative workflow settings, enabling better test-time scaling without requiring a stronger engine or an unbounded single swarm. Source: Test-Time Compute Scales Better with Parallel Agent Teams.
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Summary
25 papers screened, 15 relevant, 3 opportunities identified.
Actionable Opportunities
Cache Memory — Verifier-Gated Reasoning Caches
Paper: CPU-ARC: Agentic Reasoning Caching for on-CPU Reasoning Reuse
Authors: Peter Belcak, Greg Heinrich, Saurav Muralidharan
Published: 2026-11-06
Effort: high
Rationale: CPU-ARC separates expensive solution discovery from cheap application and reports 16–291× lower invocation cost while preserving frontier-model performance on its evaluated tasks. gh-aw could persist only procedures that pass workflow-owned verification, then reuse them through cache-memory for structurally equivalent tasks, reducing tokens and latency without treating unverified model output as executable truth.
Developer Experience — Instruction-Freshness Audit
Paper: When Instruction Files Go Stale: Documentation Debt and Hidden Cost in Coding Agents
Authors: Tural Mehtiyev, Biruk Tadesse, Lawrence Arkoh
Published: 2026-11-06
Effort: medium
Rationale: The paper identifies stale AGENTS.md- and CLAUDE.md-style guidance as recurring documentation debt whose cost is paid in every later coding-agent session but can be hidden by task-success-only evaluation. gh-aw can add a preflight or scheduled audit that cross-checks instruction references against repository state and uses session traces to surface repeated recovery work, reducing wasted tokens and misleading agent behavior.
Sub-Agents — Parallel Team Orchestration
Paper: Test-Time Compute Scales Better with Parallel Agent Teams
Authors: Junlin Wang, Haozhe Chen, Ang Li
Published: 2026-11-06
Effort: medium
Rationale: The paper finds that many small collaborating teams with verifier-based selection outperform both independent best-of-N agents and one large team on long-horizon tasks. gh-aw can expose team size, team count, and grader selection as declarative workflow settings, enabling better test-time scaling without requiring a stronger engine or an unbounded single swarm.
Papers Analyzed
Next Steps
Quick-Win Agentic Prompts
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