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cs.LG2026

PatchBench: Measuring Collateral Damage in Activation Patching

Alexi Canesse, Mathis Le Bail, Maël Jenny +3

An LLM safety patch can pass a benchmark while still being a poor repair. This risk is especially acute for jailbreak repairs, where the goal is to correct a specific unsafe behavi…

cs.CR2026

RAISED: Self-Distillation for Robustness to Prompt Injection in LLM Agents

Mohamed Dhouib, Clement Elliker, Alexi Canesse +6

Tool-using language-model agents are vulnerable to indirect prompt injection because they must act on untrusted external content. Existing training-time defenses can reduce attack…

cs.CL2026

Kernel Token Contradiction: a Fast and Principled Approach for LLM Claim Uncertainty Quantification

Jérémie Dentan, Alexi Canesse, Mahammed El Sharkawy +1

Claim-level Uncertainty Quantification (UQ) aims to mitigate the lack of reliability of Large Language Models (LLMs) by evaluating the factuality of each claim in their outputs. We…

cs.MA2026

Decoupling Communication from Policy: Robust MARL under Bandwidth Constraints

Alexi Canesse, Benoît Goupil, Jesse Read +1

Communication enables coordination in multi-agent reinforcement learning (MARL), but many real-world applications, e.g., search-and-rescue with drone swarms, operate under severe b…

math.NA2026

Supervised and Unsupervised Neural Network Solver for First Order Hyperbolic Nonlinear PDEs

Zakaria Baba, Alexandre M. Bayen, Alexi Canesse +7

We present a neural network-based method for learning scalar hyperbolic conservation laws. Our method replaces the traditional numerical flux in finite volume schemes with a traina…