arXiv:2512. 05013v2 Announce Type: replace Abstract: Generative models augmented with external tools and update mechanisms (or \textit{agents}) have demonstrated capabilities beyond intelligent prompting of base models.
arXiv:2606. 30801v1 Announce Type: cross Abstract: Personalization algorithms determine what content users encounter on online platforms.
arXiv:2603. 00829v2 Announce Type: replace-cross Abstract: Safe deployment of Large Language Model (LLM) agents in autonomous settings requires reliable oversight mechanisms.
The paper examines whether internal representations of agentic systems can better indicate task success than traditional confidence measures. It introduces two methods—Latent Trajectory Dynamics (LTD) and Action Representation Probe (ARP)—that analyze changes in residual-stream representations and action-level representations, respectively. Experiments on Bash, SQL, and Python benchmarks with Qwen and DeepSeek models show these methods outperform conventional surface-level and sequence-based calibration baselines, offering a zero‑overhead reliability monitor without prompt changes or multiple rollouts.
arXiv:2606. 15306v1 Announce Type: cross Abstract: We envision continually learning agentic systems that become more useful over time: as they encounter sequences of related tasks, they should infer the hidden structure shared across those tasks and use it to improve future decisions.
arXiv:2607. 11228v1 Announce Type: cross Abstract: While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases.
EvoTS-Agent is a self‑evolving large language model agent designed for autonomous change‑point detection in financial time series. It begins with curated exploratory data analysis to set up candidate models, then iteratively refines detection pipelines using three operators—Revision, Alternative Strategy, and Recombination—guided by validation feedback. Across four benchmark datasets, EvoTS-Agent consistently outperforms existing LLM‑based agents and achieves a 100% execution success rate with all tested backbone LLMs.
Deep Noir is a framework that autonomously discovers optimal activation‑steering parameters in transformer models by leveraging Logit Lens convergence and causal head‑level attribution. It demonstrates significant performance gains across models ranging from 1B to 9B parameters, improving spam detection by up to 42 percentage points and SST‑2 sentiment classification by 13.1 percentage points without code changes. The approach also reveals that increased steering magnitude expands a predictable prompt‑injection attack surface, highlighting security implications for agent systems using steered classifiers.
While Large Vision-Language Models (LVLMs) demonstrate remarkable capabilities, they remain highly susceptible to embedded social biases. Existing bias evaluation protocols predominantly rely on static datasets, which provide only a superficial assessment, as their fixed test cases cannot adaptively evolve to measure the true depth and limits of model vulnerabilities.
ReLiveGym is a diagnostic environment that evaluates long‑lived language‑model agents over weeks of chronologically replayed real‑world streams such as news, market data, and social media. The tasks vary in time sensitivity, reasoning depth, and recurrence, and the study tests eight base language models to see how model choice and harness design—especially action timing—affect performance. Continuous learning from hindsight feedback is also examined to address failure modes in these long‑term tasks.
arXiv:2606. 05684v1 Announce Type: new Abstract: A central challenge for language agents is utilizing past experience to adapt to dynamic test-time conditions.
Text files such as skill files, memory files, and behavioral configuration files play a central role in defining how modern agents act. Through edits by humans or the agents themselves, these files may evolve over time, directly steering the agent's behavior in future interactions.
Self-evolving agents can continually improve their behavior, while tools define the executable action space through which they interact with the environment. However, exposing the full tool library to...