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token-optimization — Bounded-Context Autocompaction for Long Sub-Agent Sessions
Paper: CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents Authors: Trang Nguyen, Eulrang Cho, Bingqing Chen Published: 2026-09-22 Effort: medium Rationale: CliffCompaction shows that autocompaction under a bounded context cuts cost by up to 50% while maintaining or improving performance on long-horizon coding tasks (Terminal-Bench, KernelBench). gh-aw's cache-memory and sub-agent fan-out currently retain full session history across turns; adopting a similar bounded-context compaction technique in the cache-memory pipeline would directly reduce token spend for long-running sub-agent workflows without a corresponding quality loss.
token-optimization — Fast Typed Decision Layer Before Strong-Model Escalation
Paper: REFLEX with Jev for Efficient Selective Control in LLM Agents Authors: Tiantong Wu, Wei Yang Bryan Lim Published: 2026-09-22 Effort: medium Rationale: REFLEX shows a fast, typed decision layer (Jev) can replace 72.7% of strong-model calls while retaining 95% task success by handling bounded decisions locally and escalating only on low confidence. gh-aw's sub-agent orchestration and workflow compiler could adopt a similar pre-call routing/confidence gate before dispatching to Claude/Copilot/Gemini/Codex, reducing cost for routine, low-ambiguity decisions.
other — Failure-Informed Runtime Policies at Known Failure States
Paper: FIRE: Failure-Informed Runtime Engineering for Reliable Language-Model Agents Authors: Nikita Agarwal, Nivedit Jain Published: 2026-09-22 Effort: medium Rationale: FIRE converts observed failure patterns into targeted runtime policies (natural-language instructions and action denials) applied by the harness at states preceding past failures, raising repeated-success rates without retraining or prompt changes. gh-aw could log failure-preceding states across workflow runs and inject corrective guardrails at those exact points in future runs, improving reliability deterministically without touching the base prompt or engine.
Investigate: Bounded-context autocompaction for cache-memory/sub-agent sessions (effort: medium)
Investigate: Fast typed decision layer to reduce strong-model escalations (effort: medium)
Investigate: Failure-informed runtime policies at known failure states (effort: medium)
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: Add a bounded-context autocompaction step to sub-agent/cache-memory sessions that summarizes and prunes long execution history under a token budget while preserving task-critical context. in gh-aw's token-optimization component. Rationale: CliffCompaction shows that autocompaction under a bounded context cuts cost by up to 50% while maintaining or improving performance on long-horizon coding tasks (Terminal-Bench, KernelBench). gh-aw's cache-memory and sub-agent fan-out currently retain full session history across turns; adopting a similar bounded-context compaction technique in the cache-memory pipeline would directly reduce token spend for long-running sub-agent workflows without a corresponding quality loss.. Source: CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents.
@copilot Implement: Insert a fast, typed decision layer ahead of sub-agent/engine calls that only escalates to the full LLM engine when confidence is low or generation is required, cutting strong-model invocations. in gh-aw's token-optimization component. Rationale: REFLEX shows a fast, typed decision layer (Jev) can replace 72.7% of strong-model calls while retaining 95% task success by handling bounded decisions locally and escalating only on low confidence. gh-aw's sub-agent orchestration and workflow compiler could adopt a similar pre-call routing/confidence gate before dispatching to Claude/Copilot/Gemini/Codex, reducing cost for routine, low-ambiguity decisions.. Source: REFLEX with Jev for Efficient Selective Control in LLM Agents.
@copilot Implement: Add a failure-informed runtime policy layer that applies targeted natural-language instructions and action denials at workflow states known to precede past failures, without modifying the base prompt or engine. in gh-aw's other component. Rationale: FIRE converts observed failure patterns into targeted runtime policies (natural-language instructions and action denials) applied by the harness at states preceding past failures, raising repeated-success rates without retraining or prompt changes. gh-aw could log failure-preceding states across workflow runs and inject corrective guardrails at those exact points in future runs, improving reliability deterministically without touching the base prompt or engine.. Source: FIRE: Failure-Informed Runtime Engineering for Reliable Language-Model Agents.
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Summary
25 papers screened, 11 relevant, 3 opportunities identified.
Actionable Opportunities
token-optimization — Bounded-Context Autocompaction for Long Sub-Agent Sessions
Paper: CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents
Authors: Trang Nguyen, Eulrang Cho, Bingqing Chen
Published: 2026-09-22
Effort: medium
Rationale: CliffCompaction shows that autocompaction under a bounded context cuts cost by up to 50% while maintaining or improving performance on long-horizon coding tasks (Terminal-Bench, KernelBench). gh-aw's cache-memory and sub-agent fan-out currently retain full session history across turns; adopting a similar bounded-context compaction technique in the cache-memory pipeline would directly reduce token spend for long-running sub-agent workflows without a corresponding quality loss.
token-optimization — Fast Typed Decision Layer Before Strong-Model Escalation
Paper: REFLEX with Jev for Efficient Selective Control in LLM Agents
Authors: Tiantong Wu, Wei Yang Bryan Lim
Published: 2026-09-22
Effort: medium
Rationale: REFLEX shows a fast, typed decision layer (Jev) can replace 72.7% of strong-model calls while retaining 95% task success by handling bounded decisions locally and escalating only on low confidence. gh-aw's sub-agent orchestration and workflow compiler could adopt a similar pre-call routing/confidence gate before dispatching to Claude/Copilot/Gemini/Codex, reducing cost for routine, low-ambiguity decisions.
other — Failure-Informed Runtime Policies at Known Failure States
Paper: FIRE: Failure-Informed Runtime Engineering for Reliable Language-Model Agents
Authors: Nikita Agarwal, Nivedit Jain
Published: 2026-09-22
Effort: medium
Rationale: FIRE converts observed failure patterns into targeted runtime policies (natural-language instructions and action denials) applied by the harness at states preceding past failures, raising repeated-success rates without retraining or prompt changes. gh-aw could log failure-preceding states across workflow runs and inject corrective guardrails at those exact points in future runs, improving reliability deterministically without touching the base prompt or engine.
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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