arXiv AI
Jul 29
Similar Models Learn Differently: Final-Window Pretraining Shapes Post-Training Beyond SFT
arXiv:2607. 25063v1 Announce Type: new Abstract: Developers judge a model checkpoint by how it behaves.
Moving the Mean Toward the Known Good, Not Beyond It: What Inference-Time Interventions and Weight Consolidation Buy in Open-Ended Generation
The study investigates how inference‑time interventions and weight consolidation affect open‑ended generation in an online bin‑packing task. By iteratively generating, verifying, selecting, and consolidating with LoRA, the model’s outputs shift toward higher value, reducing excess by 1.7 points and outperforming random consolidation by 3.1 points. Across three independent runs, the mean performance remained consistent, and the best candidates converged to the classic heuristic’s level without exceeding it, while consolidation also lowered the proportion of better‑than‑classic candidates but increased their absolute number.
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arXiv AI
Jul 14
The Verifier is the Curriculum: Execution-Gated Self-Distillation for Cross-Family Game Generation
arXiv:2607. 09709v1 Announce Type: new Abstract: Post-training a code generator against a learned judge can optimize proxy features that raise the score without improving the artifact.
arXiv AI
Aug 21
Evidence Before Expansion: Reuse, Spawn, or Defer in Lifelong Expert Pools
arXiv:2608. 19888v1 Announce Type: cross Abstract: Streaming systems that maintain a pool of expert models must repeatedly decide whether to reuse an existing expert for arriving data, spawn a new one, or defer.
arXiv AI
Jun 29
When Is an LLM Worth It for Hyperparameter Optimization? A Budget-Matched Study on Tabular Data Finds the Warm-Start Is a Default Configuration, Not the Model
arXiv:2606. 21641v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have been proposed as hyperparameter-optimization (HPO) advisors that "warm-start" search from prior knowledge, proposing strong configurations in very few evaluations.
arXiv Machine Learning
Aug 3
ALIVE: Warnings Before Exclusion in Budgeted Multi-Source Learning
arXiv:2607. 29400v1 Announce Type: new Abstract: A routing decision can be revised at the next transaction, but a latched source exclusion persists across later decisions.
arXiv AI
Sep 2
Commit-first LLM judging inherits the judge's own errors
The paper investigates whether widely used evaluation frameworks for large language models (LLMs) implement a defense called commit‑first judging, which requires a judge to solve a task itself before accepting a candidate answer. Across 24 configurations in eight popular frameworks, none use the full commit‑first method; nine use a weaker variant that is ineffective. In controlled experiments, the weaker variant allowed systems to game the judge, while the full commit‑first approach eliminated this vulnerability but sometimes worsened evaluation when the judge’s own answer was incorrect.
arXiv Computation and Language
Sep 23
Self-Cleaning and Captured Anyway: One Measured Primitive for Error in a Store an Agent Writes to Itself, and What a Falling Score Actually Measures
arXiv:2609.25052v1 Announce Type: new Abstract: "An agent that writes its conclusions into a store it later retrieves from closes a loop usually reported as one-way contamination. Taking the loop to...
arXiv AI
Sep 16
The Verifier is the Curriculum: Precision Sets the Return on Search in Code Self-Distillation
arXiv:2607.09709v2 Announce Type: replace Abstract: Post-training a code generator against a learned judge can optimize proxy features that raise the score without improving the artifact. We study th...
arXiv AI
5d ago
Trace Evolution Bench: Measuring Whether Agents Improve from Their Own Environment Traces
arXiv:2610.15055v1 Announce Type: new Abstract: Self-evolving agents are usually scored by endpoint Gain: held-out success after an update minus success before. That difference can credit a frozen re...
arXiv AI
Jul 31
One Run Is Not an Idea: The Implementation Lottery in Automated Research
arXiv:2607. 26587v1 Announce Type: cross Abstract: Automated research systems use experimental scores both to deliver artifacts and to decide which ideas to retain, transfer, and pursue.
arXiv AI
Oct 9
Who Verifies the Verifier? Co-Evolving Inspectable Graders with Self-Improving Agents
The paper introduces a method for co-evolving inspectable verifiers alongside self-improving agents. By synthesizing verifiers from clustered failures and selecting them based on agreement with a reference set, the authors demonstrate improved held‑out agreement on MBPP+ and outperform a bare LLM judge. They also show that removing anchor guards collapses the verifier into a vacuous grader, yet the collapsed verifier still trains skills effectively, indicating that downstream task scores cannot certify a self‑evolved verifier.
arXiv AI
5d ago
Verifier-Harness Co-Evolution for Training-Free Self-Improvement on Open-Ended Tasks
arXiv:2610.17609v1 Announce Type: new Abstract: Training-free self-improvement lets an agent use feedback to revise the response it is about to return and to update the harness state that shapes how...