arXiv Machine Learning June 11, 2026 By Samuel Tetteh, Cody Fleming

Seeing Before Colliding: Anticipatory Safe RL with Frozen Vision-Language Models

Read the original on arXiv Machine Learning →

arXiv:2606. 11266v1 Announce Type: new Abstract: The cost signal that constrained-RL algorithms optimize against is almost always reactive: the simulator emits a non-zero cost only after a collision has begun, and the Lagrange multiplier of PPO-Lagrangian grows only after the episode budget has been exceeded.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Computation and Language

Sep 2

From Base Rollouts to RL Reasoning: A Budgeted Search Perspective

arXiv:2609.01274v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves language-model reasoning, but how these gains relate to inference-time decoding and sear...

By Wenhe Sun, Cunxiang Wang, Zijun Yao, Yixin Cao

reinforcement-learning benchmarks

arXiv AI

Jul 14

YUKTI: From Natural-Language Situations to Robust, Verifiable Decisions An Uncertainty-Typed Proposition IR, Assumption-Robust Pareto Frontiers, and a Regret Certificate

arXiv:2607. 09706v1 Announce Type: new Abstract: Language models turn a worded situation into a numeric plan, and the dominant pipelines (NL4Opt, OptiMUS, ORLM, OR-LLM-Agent) commit to a single objective and point-valued coefficients, then solve once.

By Suyash Mishra

llms agents benchmarks

arXiv AI

Aug 21

FMT$^{\mathrm{X}}$: Lazy Wavefront Search for Dynamic Replanning

arXiv:2509. 08521v2 Announce Type: replace-cross Abstract: FMT$^{*}$ plans efficiently in static worlds by expanding a cost-ordered wavefront and collision-checking lazily, but its single-pass unvisited rule cannot revise paths when obstacles change.

By Soheil Espahbodi Nia

arXiv AI

Jul 21

Constrained Path Reasoning: Measuring When Committed Stages Earn Their Cost

arXiv:2607. 17240v1 Announce Type: new Abstract: When does a committed intermediate stage in an LLM reasoning pipeline earn its cost?

By Honglin Li (ShanghaiTech University)

llms

arXiv Machine Learning

Jun 16

Selection Without Signal, Recovery Through Expression: A Measurement Study of Post-Hoc Falsification Operators for Frozen Small Code Models

arXiv:2606. 16999v1 Announce Type: cross Abstract: Frozen small code models ( =45.

By Mehmet Iscan

benchmarks safety

arXiv AI

Sep 25

Operator Packages, Proposer Strength, and Construction-Family Plateaus in Office-Scale Verified Search

The paper reports on a large‑scale verified search experiment using a 30B language model on a laptop, evaluating three operator packages—schematic notebooks, named obstacles, and behavioural repulsion—in a factorial design across nine construction problems. Results show that the full composition of operators closes the seed‑to‑record gap more effectively than any single component, increases construction‑hash diversity, and that memory plus repulsion consistently avoids collapse. A frontier proposer achieves similar gains in far fewer samples, but the search ultimately stalls near a plateau where the reference family is adopted and optimized only when provided as code.

By Roberto I. Ono Filho

llms robotics

One email a morning, machine-written

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