New research on AI and security, selected for relevance to language models, AI agents and cybersecurity. Summaries are the authors’ or publisher’s own words, linked to the original.
Peer-reviewed
Deep learning perturbation models can outperform baselines on calibrated metrics
Times are changing but order matters: transferable prediction of small-molecule liquid chromatography retention times
Shifting from knowledge retrieval to evidence exploration and synthesis
WeedNet: a foundation model-based global-to-local AI approach for weed species identification and classification
This award-winning microscopy image used AI — igniting controversy in a prestigious competition
AI ‘speech clock’ assesses how fast you’re ageing from your voice
New preprints (not yet peer-reviewed)
ArgGYM: A Procedural, Engine-Verified Benchmark for Structured Defeasible Reasoning
Recent progress in large language model reasoning has been driven by benchmarks and reinforcement learning environments with automatically verifiable rewards, particularly in mathematics, code, and formal logic. These settings make model accuracy easier to evaluate and optimize, but it remains unclear how far success under fixed problem specifications and stable evaluation criteria transfers to reasoning outside such domains.
PhantomEnvironments: Training LLM Agents in Fictional Worlds
Training LLM agents with reinforcement learning (RL) is bottlenecked by environments, which must provide verifiable rewards, support long-horizon interaction, and scale cheaply. Existing approaches rely on costly human-curated data or on LLM-generated environments that risk hallucinations and benchmark contamination.
VirusCascade: Hijacking Collaborative Reflection in LLM-Powered Recommender Agents
Advancing beyond traditional static scoring models, LLM-powered agentic recommender systems (LLM-ARS) instantiate users and items as autonomous agents, whose semantic states are dynamically refined through a recurrent process known as collaborative reflection. While this mechanism improves recommendation quality, it simultaneously introduces a systemic vulnerability: adversarial evidence injected into a single agent can be rationalised into a legitimate preference narrative, written back into memory, and propagated to other agents through interaction contexts.
Emergent alignment and the projectability of ethical personas
Work on `emergent misalignment' shows that finetuning LLMs on narrow tasks can induce broadly misaligned behavior. This supports the `persona selection' (PSM) hypothesis: during pre-training, LLMs learn to simulate different characters/perspectives, which can be elicited and refined during post-training.
Aligned Data Can Induce Misalignment via Context Confusion
Large language models (LLMs) are frequently updated for various use cases, where filtering out misaligned training samples is a common practice for preventing post-update misalignment. However, alignment is inherently context-dependent: a recommendation that is aligned in one context may be inappropriate in another.
Large Knowledge Model: A Knowledge Foundation for Agentic Science at Scale
Agentic science envisions many autonomous agents investigating concurrently while building on a shared, evolving body of scientific knowledge. This requires a knowledge foundation that supports high-concurrency access, preserves traceable and reusable reasoning, and grows incrementally.
SURE: Framework for Safety to Construct Trustworthy AI
Warning: This paper contains harmful and offensive text. Recently, large language models such as GPT-4, and Claude have revolutionized tasks in various domains.
AgentSnare: Learning to Delay, Divert, and Defuse Autonomous Penetration Agents
Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools. This dependence allows defenders to inject deceptive observations that can mislead the agent's decision-making process.