OpenRaiser/Intern-Atlas huggingface dataset:
Emergent human-like working memory from artificial neurons with intrinsic plasticity
Venue: arXiv cs.ET 2026
Year: 2026

Working memory enables the brain to integrate transient information for rapid decision-making. Artificial networks typically replicate this via recurrent or parallel architectures, yet incur high energy costs and noise sensitivity. Here we report IPNet, a hardware-software co-designed neuromorphic architecture realizing human-like working memory via neuronal intrinsic plasticity. Exploiting Joule-heating dynamics of Magnetic Tunnel Junctions (MTJs), IPNet physically emulates biological memory volatility. The memory behavior of the proposed architecture shows similar trends in n-back, free recall and memory interference tasks to that of reported human subjects. Implemented exclusively with MTJ neurons, the architecture with human-like working memory achieves 99.65% accuracy on 11-class DVS gesture datasets and maintains 99.48% on a novel 22-class time-reversed benchmark, outperforming RNN, LSTM, and 2+1D CNN baselines sharing identical backbones. For autonomous driving (DDD-20), IPNet reduces steering prediction error by 14.4% compared to ResNet-LSTM. Architecturally, we identify a 'Memory-at-the-Frontier' effect where performance is maximized at the sensing interface, validating a bio-plausible near-sensor processing paradigm. Crucially, all results rely on raw parameters from fabricated devices without optimization. Hardware-in-the-loop validation confirms the system's physical realizability. Separately, energy analysis reveals a reduction in memory power of 2,874x compared to LSTMs and 90,920x versus parallel 3D-CNNs. This capacitor-free design enables a compact ~1.5um2 footprint (28 nm CMOS): a >20-fold reduction over standard LIF neurons. Ultimately, we demonstrate that instantiating human-like working memory via intrinsic neuronal plasticity endows neural networks with the dual biological advantages of superior dynamic vision processing and minimal metabolic cost.
DSO: Direct Steering Optimization for Bias Mitigation
Venue: arXiv cs.LG 2026
Year: 2026

Generative models are often deployed to make decisions on behalf of users, such as vision-language models (VLMs) identifying which person in a room is a doctor to help visually impaired individuals. Yet, VLM decisions are influenced by the perceived demographic attributes of people in the input, which can lead to biased outcomes like failing to identify women as doctors. Moreover, when reducing bias leads to performance loss, users may have varying needs for balancing bias mitigation with overall model capabilities, highlighting the demand for methods that enable controllable bias reduction during inference. Activation steering is a popular approach for inference-time controllability that has shown potential in inducing safer behavior in large language models (LLMs). However, we observe that current steering methods struggle to correct biases, where equiprobable outcomes across demographic groups are required. To address this, we propose Direct Steering Optimization (DSO) which uses reinforcement learning to find linear transformations for steering activations, tailored to mitigate bias while maintaining control over model performance. We demonstrate that DSO achieves state-of-the-art trade-off between fairness and capabilities on both VLMs and LLMs, while offering practitioners inference-time control over the trade-off. Overall, our work highlights the benefit of designing steering strategies that are directly optimized to control model behavior, providing more effective bias intervention than methods that rely on pre-defined heuristics for controllability.
OLAF: Towards Robust LLM-Based Annotation Framework in Empirical Software Engineering
Venue: arXiv cs.SE 2026
Year: 2026

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the \textbf{Operationalization for LLM-based Annotation Framework (OLAF)}, a conceptual framework that organizes key constructs: \textit{reliability, calibration, drift, consensus, aggregation}, and \textit{transparency}. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.
Examining the Utility of Self-disclosure Types for Modeling Annotators of Social Norms
Venue: arXiv cs.CL 2026
Year: 2026

Recent work has explored the use of personal information in the form of persona sentences or self-disclosures to improve modeling of individual characteristics and prediction of annotator labels for subjective tasks. The volume of personal information has historically been restricted and thus little exploration has gone into understanding what kind of information is most informative for predicting annotator labels. In this work, we categorize self-disclosures and use them to build annotator models for predicting judgments of social norms. We perform several ablations and analyses to examine the impact of the type of information on our ability to predict annotation patterns. Contrary to previous work, only a small number of comments related to the original post are needed. Lastly, a more diverse sample of annotator self-disclosures did not lead to the best performance. Sampling from a larger pool of comments without filtering still yields the best performance, suggesting that there is still much to uncover in terms of what information about an annotator is most useful for verdict prediction.
Ev-Trust: An Evolutionary Stable Trust Mechanism for Decentralized LLM-Based Multi-Agent Service Economies
Venue: arXiv cs.MA 2026
Year: 2026

Autonomous LLM-based agents are increasingly engaging in decentralized service interactions to collaboratively execute complex tasks. However, the intrinsic instability and low-cost generativity of LLMs introduce a systemic vulnerability, where self-interested agents are incentivized to pursue short-term gains through deceptive behaviors. Such strategies can rapidly proliferate within the population and precipitate a systemic trust collapse. To address this, we propose Ev-Trust, a strategy-equilibrium trust mechanism grounded in evolutionary game theory. Ev-Trust constructs a dynamic feedback loop that couples trust evaluation with evolutionary incentives, embedding interaction history and reputation directly into the agent's expected revenue function. This mechanism fundamentally reshapes the revenue structure, converting trustworthiness into a decisive survival advantage that suppresses short-sightedness. We provide a rigorous theoretical foundation based on the Replicator Dynamics, proving the asymptotic stability of Evolutionary Stable Strategies (ESS) that favor cooperation. Experimental results indicate that Ev-Trust effectively eliminates malicious strategies and enhances collective revenue, exhibiting resilience against the invasion of mutant behaviors.
A Domain-Adapted Pipeline for Structured Information Extraction from Police Incident Announcements on Social Media
Venue: arXiv cs.CL 2026
Year: 2026

Structured information extraction from police incident announcements is crucial for timely and accurate data processing, yet presents considerable challenges due to the variability and informal nature of textual sources such as social media posts. To address these challenges, we developed a domain-adapted extraction pipeline that leverages targeted prompt engineering with parameter-efficient fine-tuning of the Qwen2.5-7B model using Low-Rank Adaptation (LoRA). This approach enables the model to handle noisy, heterogeneous text while reliably extracting 15 key fields, including location, event characteristics, and impact assessment, from a high-quality, manually annotated dataset of 4,933 instances derived from 27,822 police briefing posts on Chinese Weibo (2019-2020). Experimental results demonstrated that LoRA-based fine-tuning significantly improved performance over both the base and instruction-tuned models, achieving an accuracy exceeding 98.36% for mortality detection and Exact Match Rates of 95.31% for fatality counts and 95.54% for province-level location extraction. The proposed pipeline thus provides a validated and efficient solution for multi-task structured information extraction in specialized domains, offering a practical framework for transforming unstructured text into reliable structured data in social science research.
The Agony of Opacity: Foundations for Reflective Interpretability in AI-Mediated Mental Health Support
Venue: arXiv cs.HC 2026
Year: 2026

Throughout history, a prevailing paradigm in mental healthcare has been one in which distressed people may receive treatment with little understanding around how their experience is perceived by their care provider, and in turn, the decisions made by their provider around how treatment will progress. Paralleling this offline model of care, people who seek mental health support from artificial intelligence (AI)-based chatbots are similarly provided little context for how their expressions of distress are processed by the model, and subsequently, any reasoning or theoretical grounding that may underlie model responses. People in severe distress who turn to AI chatbots for support thus find themselves caught between black boxes, contending with unique forms of agony that arise from these intersecting opacities. In this paper, we argue that the distinct psychological state of individuals experiencing severe mental distress uniquely necessitates a higher standard of end-user interpretability in comparison to general AI chatbot use. We propose a reflective interpretability approach to AI-mediated mental health support, which nudges users to engage in an agency-preserving and iterative process of reflection and interpretation of model outputs, towards creating meaning from interactions (rather than accepting outputs as directive instructions). Drawing on interpretability practices from four mental health fields (psychotherapy, crisis intervention, psychiatry, and care authorization), we describe concrete design approaches for reflective interpretability in AI-mediated mental health support, including role induction, prosocial advance directives, intervention titration, and well-defined mechanisms for recourse, alongside a discussion of potential risks and mitigation measures.
CALM: A CKA-Guided Adaptive Layer-Wise Modularization Framework for LLM Quantization
Venue: arXiv cs.LG 2026
Year: 2026

Current mainstream post-training quantization methods for large language models typically apply a uniform quantization strategy across all network layers, overlooking the substantial differences in algorithmic suitability among layers. To address this limitation, we propose CALM (A CKA-guided Adaptive Layer-wise Modularization)a fine-tuning-free, plug-and-play framework for algorithmic heterogeneous quantization. CALM independently evaluates multiple PTQ algorithms on each layer and employs Linear Centered Kernel Alignment (CKA) as a metric to automatically select the optimal quantization strategy per layer. The individually optimized strategies are then integrated to construct a hybrid quantized model. Experiments demonstrate that our approach consistently outperforms both uniform quantization baselines and state-of-the-art mixed-precision methods across mainstream LLMsincluding LLaMA and Qwenin terms of perplexity (PPL) and downstream task performance.
Pretrained battery transformer (PBT): A foundation model for universal battery life prediction
Venue: arXiv cs.LG 2026
Year: 2026

Early prediction of battery cycle life is essential for improving battery design, manufacturing, and deployment. However, despite encouraging results with machine learning, progress remains constrained by scarce data and data heterogeneity across battery chemistries, specifications, formation protocols, and operating conditions. Although transfer learning has been widely explored to alleviate these challenges, its effectiveness is constrained by the lack of a foundation model that can capture broadly transferable knowledge from diverse battery life data. This gap persists because integration of heterogeneous battery datasets under data scarcity is inherently challenging. Here we introduce the pretrained battery transformer (PBT), a foundation model for battery life prediction that incorporates battery-knowledge-encoded mixture-of-experts layers to learn transferable representations from heterogeneous data. PBT is pretrained on 13 lithium-ion battery datasets and subsequently adapted to downstream battery life prediction tasks through transfer learning. Across 15 datasets covering 977 batteries and 533 sets of aging conditions from lithium-ion, sodium-ion and zinc-ion batteries, PBT achieves state-of-the-art performance, surpassing the strongest competing method by 21.8% on average, with gains of up to 86.9%. Our study establishes the first foundation model for battery life prediction and provides a scalable route towards universal battery lifetime prediction systems, with broader implications for other scientific and technological domains characterized by scarce and heterogeneous data.
CountZES: Counting via Zero-Shot Exemplar Selection
Venue: arXiv cs.CV 2026
Year: 2026

Object counting in complex scenes is particularly challenging in the zero-shot (ZS) setting, where instances of unseen categories are counted using only a class name. Existing ZS counting methods that infer exemplars from text often rely on off-the-shelf open-vocabulary detectors (OVDs), which in dense scenes suffer from semantic noise, appearance variability, and frequent multi-instance proposals. Alternatively, random image-patch sampling is employed, which fails to accurately delineate object instances. To address these issues, we propose CountZES, an inference-only approach for object counting via ZS exemplar selection. CountZES discovers diverse exemplars through three synergistic stages: Detection-Anchored Exemplar (DAE), Density-Guided Exemplar (DGE), and Feature-Consensus Exemplar (FCE). DAE refines OVD detections to isolate precise single-instance exemplars. DGE introduces a density-driven, self-supervised paradigm to identify statistically consistent and semantically compact exemplars, while FCE reinforces visual coherence through feature-space clustering. Together, these stages yield a complementary exemplar set that balances textual grounding, count consistency, and feature representativeness. Experiments on diverse datasets demonstrate CountZES superior performance among ZOC methods while generalizing effectively across domains.
Refusal Steering: Fine-grained Control over LLM Refusal Behaviour for Sensitive Topics
Venue: arXiv cs.CL 2026
Year: 2026

We introduce Refusal Steering, an inference-time method to exercise fine-grained control over Large Language Models refusal behaviour on politically sensitive topics without retraining. We replace fragile pattern-based refusal detection with an LLM-as-a-judge that assigns refusal confidence scores and we propose a ridge-regularized variant to compute steering vectors that better isolate the refusal--compliance direction. On Qwen3-Next-80B-A3B-Thinking, our method removes the refusal behaviour of the model around politically sensitive topics while maintaining safety on JailbreakBench and near-baseline performance on general benchmarks. The approach generalizes across 4B and 80B models and can also induce targeted refusals when desired. We analize the steering vectors and show that refusal signals concentrate in deeper layers of the transformer and are distributed across many dimensions. Together, these results demonstrate that activation steering can remove political refusal behaviour while retaining safety alignment for harmful content, offering a practical path to controllable, transparent moderation at inference time.
Vision-Language-Action Models for Autonomous Driving: Past, Present, and Future
Venue: arXiv cs.RO 2026
Year: 2026

Autonomous driving has long relied on modular "Perception-Decision-Action" pipelines, where hand-crafted interfaces and rule-based components often break down in complex or long-tailed scenarios. Their cascaded design further propagates perception errors, degrading downstream planning and control. Vision-Action (VA) models address some limitations by learning direct mappings from visual inputs to actions, but they remain opaque, sensitive to distribution shifts, and lack structured reasoning or instruction-following capabilities. Recent progress in Large Language Models (LLMs) and multimodal learning has motivated the emergence of Vision-Language-Action (VLA) frameworks, which integrate perception with language-grounded decision making. By unifying visual understanding, linguistic reasoning, and actionable outputs, VLAs offer a pathway toward more interpretable, generalizable, and human-aligned driving policies. This work provides a structured characterization of the emerging VLA landscape for autonomous driving. We trace the evolution from early VA approaches to modern VLA frameworks and organize existing methods into two principal paradigms: End-to-End VLA, which integrates perception, reasoning, and planning within a single model, and Dual-System VLA, which separates slow deliberation (via VLMs) from fast, safety-critical execution (via planners). Within these paradigms, we further distinguish subclasses such as textual vs. numerical action generators and explicit vs. implicit guidance mechanisms. We also summarize representative datasets and benchmarks for evaluating VLA-based driving systems and highlight key challenges and open directions, including robustness, interpretability, and instruction fidelity. Overall, this work aims to establish a coherent foundation for advancing human-compatible autonomous driving systems.
V-Agent: An Interactive Video Search System Using Vision-Language Models
Venue: arXiv cs.CV 2026
Year: 2026

We introduce V-Agent, a novel multi-agent platform designed for advanced video search and interactive user-system conversations. By fine-tuning a vision-language model (VLM) with a small video preference dataset and enhancing it with a retrieval vector from an image-text retrieval model, we overcome the limitations of traditional text-based retrieval systems in multimodal scenarios. The VLM-based retrieval model independently embeds video frames and audio transcriptions from an automatic speech recognition (ASR) module into a shared multimodal representation space, enabling V-Agent to interpret both visual and spoken content for context-aware video search. This system consists of three agents-a routing agent, a search agent, and a chat agent-that work collaboratively to address user intents by refining search outputs and communicating with users. The search agent utilizes the VLM-based retrieval model together with an additional re-ranking module to further enhance video retrieval quality. Our proposed framework demonstrates state-of-the-art zero-shot performance on the MultiVENT 2.0 benchmark, highlighting its potential for both academic research and real-world applications. The retrieval model and demo videos are available at https://huggingface.co/NCSOFT/multimodal-embedding.
Navigating Taxonomic Expansions of Entity Sets Driven by Knowledge Bases
Venue: arXiv cs.AI 2026
Year: 2026

Recognizing similarities among entities is central to both human cognition and computational intelligence. Within this broader landscape, Entity Set Expansion is one prominent task aimed at taking an initial set of (tuples of) entities and identifying additional ones that share relevant semantic properties with the former -- potentially repeating the process to form increasingly broader sets. However, this ``linear'' approach does not unveil the richer ``taxonomic'' structures present in knowledge resources. A recent logic-based framework introduces the notion of an expansion graph: a rooted directed acyclic graph where each node represents a semantic generalization labeled by a logical formula, and edges encode strict semantic inclusion. This structure supports taxonomic expansions of entity sets driven by knowledge bases. Yet, the potentially large size of such graphs may make full materialization impractical in real-world scenarios. To overcome this, we formalize reasoning tasks that check whether two tuples belong to comparable, incomparable, or the same nodes in the graph. Our results show that, under realistic assumptions -- such as bounding the input or limiting entity descriptions -- these tasks can be implemented efficiently. This enables local, incremental navigation of expansion graphs, supporting practical applications without requiring full graph construction.
Reinforcement Learning for Self-Improving Agent with Skill Library
Venue: arXiv cs.AI 2026
Year: 2026

Large Language Model (LLM)-based agents have demonstrated remarkable capabilities in complex reasoning and multi-turn interactions but struggle to continuously improve and adapt when deployed in new environments. One promising approach is implementing skill libraries that allow agents to learn, validate, and apply new skills. However, current skill library approaches rely primarily on LLM prompting, making consistent skill library implementation challenging. To overcome these challenges, we propose a Reinforcement Learning (RL)-based approach to enhance agents' self-improvement capabilities with a skill library. Specifically, we introduce Skill Augmented GRPO for self-Evolution (SAGE), a novel RL framework that systematically incorporates skills into learning. The framework's key component, Sequential Rollout, iteratively deploys agents across a chain of similar tasks for each rollout. As agents navigate through the task chain, skills generated from previous tasks accumulate in the library and become available for subsequent tasks. Additionally, the framework enhances skill generation and utilization through a Skill-integrated Reward that complements the original outcome-based rewards. Experimental results on AppWorld demonstrate that SAGE, when applied to supervised-finetuned model with expert experience, achieves 8.9% higher Scenario Goal Completion while requiring 26% fewer interaction steps and generating 59% fewer tokens, substantially outperforming existing approaches in both accuracy and efficiency.