handing-agent-context-editing-tools-delete-summarize-note-fold-promptHanding an agent context-editing tools (delete, summarize, note, fold) by prompt alone makes mid-size models worse than leaving their context untouched — Qwen3-8B, Qwen3-14B and Gemma4-E4B all scored lower on long-context QA with the tools than without them; only after fine-tuning on trajectories that use the tools does the toolset become a gain.
Ingested from a paper but not yet reviewed by a human. It is deliberately inert: it does not move any technique’s standing, does not count toward the backtest, and is excluded anywhere a claim would carry weight. Read the source before relying on it.
Capability: Keeping its own context clean
Observed on
Long-context QA benchmarks (NovelQA, InfinityBench En.MC, LongMemEval-S, BrowseComp+) with 8B-14B open models capped at a 32K working window; a very large model (Qwen3.5-397B-A17B).
Sources
- Measured across three base models and four benchmarks, three runs each, against a no-tools baseline at the model's full 128K window. Average scores dropped for every base model with prompt-only tools; SFT and RL versions of the same models exceeded the no-tools baseline. Does not isolate whether the loss comes from wrong tool arguments or from bad editing decisions, though a separate analysis shows high early invocation-failure rates for memory and offloading tools.
Disagreeing is the most useful thing you can do here. Both sides of every contested claim in this catalog were assembled by the same person, which is its weakest point.
Related claims
- For long-horizon tool-calling agents (tasks needing five or more calls), embedding state-transition cues — preconditions, invariants, completion states — into the descriptions of the tools on the intended chain is what actually steers the agent's trajectory; runtime corrective text appended to tool results only patches residual drift, and the plausible user prompt alone (persona, deadlines, format constraints) does not establish the trajectory at all.Following instructions hidden in data · unreviewed
- On rubric-graded long-context tasks, most of the gain from a context- compilation harness comes from extracting the context's rules, exact terms and output spec into an explicit checklist placed in the prompt, not from the executable verifiers built on top of it — and the gain appears only on rule-dense tasks and on models with enough capacity, disappearing or reversing on open-ended tasks and on a small-activation model.Losing information in long inputs · unreviewed
- A linear probe on a tool-calling model's hidden state at the last generated token detects incorrect tool-calls — including wrong-but-well-typed argument values that parsers and logs do not catch — and works better on larger models and at middle-to-late layers than at the final layer.Using the tools it is given · unreviewed
- When an agent must work over a very large tool catalogue, wrapping each tool in a natural-language interface that resolves the API schema internally — so raw schemas are retrieved on demand rather than enumerated in the model's prompt — contributes more to multi-step tool- call success than the surrounding planner, router, or verifier stages.Using the tools it is given · unreviewed
- For locally deployed open-weight models driving a stateful, dependency-ordered MCP tool server, cutting tool descriptions from full specifications (purpose, parameter semantics, constraints, failure conditions) down to one sentence each raises the fraction of calls the server rejects for every model tested, while its effect on task coverage is less consistent.Using the tools it is given · unreviewed
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: medium. Falsifier as drafted: A prompt-only agent given the same context-editing toolset matching or beating the same base model's no-tool score on these long-context QA benchmarks at 8B-14B scale. Proposed technique, not catalogued: RL-trained proactive context management with action-level credit assignment.