pith. sign in

arxiv: 2605.29668 · v1 · pith:ASVNTKU4new · submitted 2026-05-28 · 💻 cs.AI · cs.CL

GRASP: Gated Regression-Aware Skill Proposer for Self-Improving LLM Agents

Johannes Moll , Jean-Philippe Corbeil , Jiazhen Pan , Martin Hadamitzky , Daniel Rueckert , Lisa Adams , Keno Bressem This is my paper

Pith reviewed 2026-06-29 07:13 UTC · model grok-4.3

classification 💻 cs.AI cs.CL
keywords LLM agentsself-improvementskill librariesregression avoidanceclinical benchmarksprocedural knowledgeagent reliability
0
0 comments X

The pith

A regression gate on a held-out probe lets LLM agents add skills that raise success rates without losing prior performance.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

LLM agents in structured environments accumulate conflicting guidance when new natural-language notes fix one trajectory but silently harm another. GRASP generates candidate skills and admits each one only if it produces a net improvement on a balanced held-out probe while respecting a hard regression budget. On MedAgentBench the method raises gpt-oss-120b from 40.6 percent to 88.8 percent and improves every tested base model by 17 to 40 points. Ablations show that the gate, not the act of writing skills, accounts for the gains. The resulting frozen libraries transfer asymmetrically from stronger to weaker models.

Core claim

GRASP treats agent improvement as a sequence of edits to a bounded skill library. Candidate skills are generated comparatively and admitted only when they deliver a net gain on the held-out probe under a strict no-regression constraint. This gated process produces large, consistent lifts across five base models on two FHIR clinical benchmarks and transfers to three of four non-clinical environments, with libraries from stronger models improving weaker executors more than the reverse.

What carries the argument

The acceptance gate, which evaluates each candidate skill on a held-out probe and enforces a hard regression budget before any library update.

If this is right

  • Self-improvement becomes reliable because new items cannot silently degrade prior correct behavior.
  • Frozen skill libraries transfer across models, with stronger models producing skills that help weaker executors more than the reverse.
  • Gains appear on structured environments but remain flat when the action space is open-ended.
  • Skill writing without the gate performs no better than using no skills at all.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same gating logic could be applied to other self-improvement loops that currently accumulate unvalidated guidance.
  • Domains without an obvious balanced probe would need an alternative validation signal to obtain comparable reliability.
  • The observed transfer asymmetry suggests that skill generality scales with the strength of the model that proposes them.

Load-bearing premise

The held-out probe is balanced and representative enough to detect any regressions that would appear on the full task distribution.

What would settle it

A skill that passes the probe yet produces measurable performance drops when evaluated on a larger or differently distributed test set.

Figures

Figures reproduced from arXiv: 2605.29668 by Daniel Rueckert, Jean-Philippe Corbeil, Jiazhen Pan, Johannes Moll, Keno Bressem, Lisa Adams, Martin Hadamitzky.

Figure 1
Figure 1. Figure 1: Validation accuracy across training epochs [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
read the original abstract

LLM agents acting in structured environments fail in operational rather than conversational ways, and reliability depends on procedural knowledge of the environment. Prior self-improvement methods accumulate natural-language guidance without checking that each new item preserves previously correct behavior, so a note that fixes one trajectory can silently regress another. We introduce GRASP (Gated Regression-Aware Skill Proposer), which treats agent improvement as a sequence of edits to a bounded skill library, admitting each candidate only if it produces a net improvement on a balanced held-out probe under a hard regression budget. We evaluate GRASP across five base models (gpt-oss-120b, DeepSeek V4 Flash, Gemini 3.1 Flash Lite, GPT-4.1, GPT-5.4) on two FHIR-based clinical benchmarks. On MedAgentBench, GRASP lifts gpt-oss-120b from 40.6% to 88.8%, exceeds the strongest of five self-improvement baselines by 21.0 points, and improves every other base model by 17.2 to 40.3 points. Ablations attribute the gain to comparative proposal generation, the acceptance gate, and the hard regression budget rather than to skill writing itself, which without validation is no better than using no skills. The mechanism generalizes beyond the clinical domain, improving agents on three of four non-clinical environments and remaining flat only where the action space is open-ended. Frozen libraries transfer across models, where skills from a stronger model improve weaker executors beyond what they learn for themselves while the reverse does not, an asymmetry that no ungated baseline reproduces.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 3 minor

Summary. The paper introduces GRASP, which frames agent self-improvement as edits to a bounded skill library and admits candidate skills only when they produce net improvement on a balanced held-out probe under a hard regression budget. It reports large gains on two FHIR-based clinical benchmarks (e.g., lifting gpt-oss-120b from 40.6% to 88.8% on MedAgentBench, exceeding the strongest baseline by 21 points) across five base models, attributes gains via ablations to comparative proposal, the gate, and the budget (rather than skill writing alone), shows generalization to three of four non-clinical environments, and demonstrates asymmetric transfer of frozen libraries across models.

Significance. If the probe is representative, the gated mechanism provides a concrete safeguard against silent regressions that prior self-improvement methods lack, supported by ablations, cross-domain results, and the observed transfer asymmetry that ungated baselines do not reproduce. The empirical scale of the gains and the explicit regression budget make the contribution potentially impactful for reliable procedural skill acquisition in structured environments.

major comments (1)
  1. [Abstract and evaluation setup] Abstract and probe description: the central claim that the acceptance gate reliably prevents regressions rests on the held-out probe being balanced and representative of the full task distribution. The manuscript states that the probe is 'balanced' but supplies no construction procedure, stratification method, coverage check against operational failure modes, or empirical validation that it detects regressions on unseen tasks; this is load-bearing for the headline performance numbers and the assertion that skills are admitted only under a hard regression budget.
minor comments (3)
  1. [Methods] Clarify the precise definition and numerical value of the 'hard regression budget' used in all experiments, including how net improvement is computed.
  2. [Experiments] Report the size of the skill library before and after GRASP runs, and the number of candidate skills proposed per iteration.
  3. [Results] Add error bars or statistical significance tests for the reported percentage-point gains across the five base models.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive comment on the evaluation setup and for the overall positive assessment of the contribution. We agree that additional detail on the probe is required to fully support the central claims and will revise the manuscript to address this.

read point-by-point responses

  1. Referee: [Abstract and evaluation setup] Abstract and probe description: the central claim that the acceptance gate reliably prevents regressions rests on the held-out probe being balanced and representative of the full task distribution. The manuscript states that the probe is 'balanced' but supplies no construction procedure, stratification method, coverage check against operational failure modes, or empirical validation that it detects regressions on unseen tasks; this is load-bearing for the headline performance numbers and the assertion that skills are admitted only under a hard regression budget.

    Authors: We agree that the manuscript currently provides insufficient detail on probe construction, which is necessary to substantiate the regression-prevention claims. In the revised manuscript we will add a dedicated subsection in the Evaluation Setup that specifies: (1) the exact procedure for sampling and constructing the balanced held-out probe from the full task distribution, (2) the stratification method used to ensure coverage across operational failure modes, (3) any coverage or balance checks performed against the complete benchmark, and (4) empirical validation demonstrating that the probe detects regressions on tasks held out from the proposal process. These additions will directly support the load-bearing role of the acceptance gate and hard regression budget. revision: yes

Circularity Check

0 steps flagged

No circularity; gains measured on independent held-out probe

full rationale

The paper defines GRASP as admitting skills only on net improvement under a hard regression budget on a balanced held-out probe. This probe is presented as external to the proposal process, and reported gains (e.g., 40.6% to 88.8%) are measured on that probe plus separate benchmarks. No equations reduce a prediction to a fitted input by construction, no self-citation chain carries the central claim, and no ansatz or uniqueness result is imported from prior author work. The derivation therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The central claim rests on the existence of a balanced held-out probe whose construction is not detailed in the abstract and on the regression budget as a tunable control. No new physical or mathematical entities are introduced beyond the skill library concept.

free parameters (1)
  • regression budget
    Hard limit on allowed performance regression used to gate skill acceptance; value not specified in abstract.
axioms (1)
  • domain assumption The held-out probe is balanced and representative of the full task distribution.
    Net improvement and regression are measured exclusively on this probe to decide acceptance.

pith-pipeline@v0.9.1-grok · 5852 in / 1453 out tokens · 39594 ms · 2026-06-29T07:13:55.587925+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Regimes: An Auditable, Held-Out-Gated Improvement Loop Demonstrated on LongMemEval with ActiveGraph

    cs.AI 2026-06 unverdicted novelty 6.0

    Regimes uses ActiveGraph to run a target-agnostic loop that diagnoses failures, proposes pipeline repairs, and promotes them only after static checks, sandbox tests, and held-out validation, yielding +0.05 to +0.10 ac...

Reference graph

Works this paper leans on

7 extracted references · 4 canonical work pages · cited by 1 Pith paper · 3 internal anchors

  1. [1]

    Gyubok Lee, Elea Bach, Eric Yang, Tom Pollard, Alis- tair Johnson, Edward Choi, Yugang jia, and Jong Ha Lee

    Overcoming catastrophic forgetting in neural networks.Proceedings of the national academy of sciences, 114(13):3521–3526. Gyubok Lee, Elea Bach, Eric Yang, Tom Pollard, Alis- tair Johnson, Edward Choi, Yugang jia, and Jong Ha Lee. 2025. Fhir-agentbench: Benchmarking llm agents for realistic interoperable ehr question answer- ing.arXiv preprint arXiv:2509....

    work page arXiv 2025

  2. [2]

    Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus

    Agentic clinical reasoning over longitudinal myeloma records: a retrospective evaluation against expert consensus.arXiv preprint arXiv:2604.24473. Charles Packer, Sarah Wooders, Kevin Lin, Vivian Fang, Shishir G Patil, Ion Stoica, and Joseph E Gonzalez

    work page internal anchor Pith review Pith/arXiv arXiv

  3. [3]

    MemGPT: Towards LLMs as Operating Systems

    Memgpt: Towards llms as operating systems. arXiv preprint arXiv:2310.08560. JV Roig. 2025. How do llms fail in agentic scenarios? a qualitative analysis of success and failure scenarios of various llms in agentic simulations.arXiv preprint arXiv:2512.07497. Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zett...

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  4. [4]

    ReAct: Synergizing Reasoning and Acting in Language Models

    Large language models as optimizers. InIn- ternational Conference on Learning Representations, volume 2024, pages 12028–12068. Shunyu Yao, Howard Chen, John Yang, and Karthik Narasimhan. 2022a. Webshop: Towards scalable real-world web interaction with grounded language agents. InAdvances in Neural Information Process- ing Systems, volume 35, pages 20744–2...

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  5. [5]

    ECHO (Hu et al., 2025) adapts hindsight experience replay to LM agents, generating counterfactual successful trajectories from failed ones

    introduces a modular taxonomy of agent failures and a debugging framework that traces fail- ures to their root cause. ECHO (Hu et al., 2025) adapts hindsight experience replay to LM agents, generating counterfactual successful trajectories from failed ones. Experiential Reflective Learning (Allard et al., 2026) extracts heuristics from single- attempt tra...

    2025

  6. [6]

    The non-medical exper- iments in Section 4.6 draw from the AgentBench environments

    and WebArena (Zhou et al., 2024) target the web and SWE-bench (Jimenez et al., 2024) tar- gets software engineering. The non-medical exper- iments in Section 4.6 draw from the AgentBench environments. Context degradation under long inputs.A con- sequence of monotonic memory accumulation is that task-relevant guidance is diluted by outdated or redundant en...

    2024

  7. [7]

    clean" (e.g

    differ from v1, so the OOD split exercises an in- dependent set of held-out clinical workflows. Split sizes, scoring, and FHIR backend match v1. FHIR-AgentBench.A read-only structured clin- ical question-answering benchmark on MIMIC-IV FHIR data hosted on the Google Cloud Healthcare API (Lee et al., 2025). Tasks require retrieval and reasoning over FHIR r...

    2025