Here's your daily roundup of the most relevant AI and ML news for October 09, 2026. We're also covering 8 research developments. Click through to read the full articles from our curated sources.
Research & Papers
1. Constitutional Gating and Deterministic Recovery for Multi-Agent LLM Negotiation: Ablations Against a Stateful Adversarial Gatekeeper
arXiv:2610.11542v1 Announce Type: cross Abstract: Multi-agent LLM systems negotiating with a stateful counterpart waste model calls in three ways: polite loops that never meet the counterpart's hidden acceptance condition, malformed outputs that trigger retries, and compliance deadlocks in which...
Source: arXiv - AI | 10 hours ago
2. Gen-PINNs: Generative Adversarial Physics Informed Neural Networks for solving partial differential equations
arXiv:2610.10897v1 Announce Type: new Abstract: Physics-Informed Neural Networks (PINNs) are a widely used data-free method for solving Partial Differential Equations (PDEs) using machine learning. With recent advances in Generative Adversarial Networks (GANs), adversarial learning has shown str...
Source: arXiv - Machine Learning | 10 hours ago
3. LTBD: Learnable Trust-Boundary Delimiters for Prompt Injection Defense
arXiv:2610.11634v1 Announce Type: cross Abstract: Large language models (LLMs) perform remarkably well on complex tasks, yet remain highly vulnerable to prompt injection attacks, where malicious instructions embedded in external data can override user intent. Existing defenses remain limited by ...
Source: arXiv - AI | 10 hours ago
4. Generative Adversarial Loops
arXiv:2610.11458v1 Announce Type: new Abstract: AI research progress can be viewed as the interaction between two processes: benchmark creation and method discovery. Historically, both were driven by human intelligence. However, recent advances in AI have accelerated automated method discovery, ...
Source: arXiv - Machine Learning | 10 hours ago
5. Toward Optimal Regret in Adversarial MDPs with Stochastic Hard Constraints
arXiv:2610.12153v1 Announce Type: new Abstract: We study episodic constrained Markov decision processes with adversarial losses under stochastic hard constraints. Specifically, starting from a known strictly feasible policy with margin $d$, we seek to obtain optimal regret while satisfying the e...
Source: arXiv - Machine Learning | 10 hours ago
6. Closing the Horizon Gap in Policy Optimization for Adversarial MDPs
arXiv:2610.12362v1 Announce Type: new Abstract: We consider policy optimization for online episodic tabular Markov decision processes (MDPs) with adversarial losses and bandit feedback. Policy optimization updates the policy locally at each state and avoids optimization over the occupancy-measur...
Source: arXiv - Machine Learning | 10 hours ago
7. RobustLDS: Learning linear dynamical systems under adversarial corruptions
arXiv:2610.11906v1 Announce Type: cross Abstract: We consider the problem of learning linear dynamical systems under adversarial contamination from a single trajectory of length $T$. While identification of linear dynamical systems itself is well-studied, the problem of robust system identificat...
Source: arXiv - Machine Learning | 10 hours ago
8. Jailbreak Scaling Laws for Large Language Models: Polynomial-Exponential Crossover
arXiv:2603.11331v4 Announce Type: replace Abstract: Adversarial attacks can reliably steer safety-aligned large language models toward unsafe behavior. Empirically, we find that adversarial prompt-injection attacks can amplify attack success rate from the slow polynomial growth observed without ...
Source: arXiv - Machine Learning | 10 hours ago
About This Digest
This digest is automatically curated from leading AI and tech news sources, filtered for relevance to AI security and the ML ecosystem. Stories are scored and ranked based on their relevance to model security, supply chain safety, and the broader AI landscape.
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