OpenRaiser/Intern-Atlas huggingface dataset:
Post-Training LLMs as Better Decision-Making Agents: A Regret-Minimization Approach
Venue: arXiv cs.AI 2025
Year: 2025

Large language models (LLMs) are increasingly deployed as "agents" for decision-making (DM) in interactive and dynamic environments. Yet, since they were not originally designed for DM, recent studies show that LLMs can struggle even in basic online DM problems, failing to achieve low regret or an effective exploration-exploitation tradeoff. To address this, we introduce Iterative Regret-Minimization Fine-Tuning (Iterative RMFT), a post-training procedure that repeatedly distills low-regret decision trajectories back into the base model. At each iteration, the model rolls out multiple decision trajectories, selects the k-lowest regret ones, and fine-tunes itself on them. Unlike prior methods that (a) distill action sequences from known DM algorithms or (b) rely on manually crafted chain-of-thought templates, our approach leverages the regret metric to elicit the model's own DM ability and reasoning rationales. This reliance on model-generated reasoning avoids rigid output engineering and provides more flexible, natural-language training signals. Empirical results show that Iterative RMFT improves LLMs' DM performance across diverse models - from Transformers with numerical input/output, to open-weight LLMs, and advanced closed-weight models like GPT-4o mini. Its flexibility in output and reasoning formats enables generalization across tasks with varying horizons, action spaces, reward processes, and natural-language contexts. Finally, we provide theoretical insight showing that a single-layer Transformer under this paradigm can act as a no-regret learner in a simplified setting. Overall, Iterative RMFT offers a principled and general post-training framework for enhancing LLMs' decision-making capabilities.
DORAEMON: A Unified Library for Visual Object Modeling and Representation Learning at Scale
Venue: arXiv cs.CV 2025
Year: 2025

DORAEMON is an open-source PyTorch library that unifies visual object modeling and representation learning across diverse scales. A single YAML-driven workflow covers classification, retrieval and metric learning; more than 1000 pretrained backbones are exposed through a timm-compatible interface, together with modular losses, augmentations and distributed-training utilities. Reproducible recipes match or exceed reference results on ImageNet-1K, MS-Celeb-1M and Stanford online products, while one-command export to ONNX or HuggingFace bridges research and deployment. By consolidating datasets, models, and training techniques into one platform, DORAEMON offers a scalable foundation for rapid experimentation in visual recognition and representation learning, enabling efficient transfer of research advances to real-world applications. The repository is available at https://github.com/wuji3/DORAEMON.
On the Equivalence of Regression and Classification
Venue: arXiv cs.LG 2025
Year: 2025

A formal link between regression and classification has been tenuous. Even though the margin maximization term $\|w\|$ is used in support vector regression, it has at best been justified as a regularizer. We show that a regression problem with $M$ samples lying on a hyperplane has a one-to-one equivalence with a linearly separable classification task with $2M$ samples. We show that margin maximization on the equivalent classification task leads to a different regression formulation than traditionally used. Using the equivalence, we demonstrate a ``regressability'' measure, that can be used to estimate the difficulty of regressing a dataset, without needing to first learn a model for it. We use the equivalence to train neural networks to learn a linearizing map, that transforms input variables into a space where a linear regressor is adequate.
V-Thinker: Interactive Thinking with Images
Venue: arXiv cs.CV 2025
Year: 2025

Empowering Large Multimodal Models (LMMs) to deeply integrate image interaction with long-horizon reasoning capabilities remains a long-standing challenge in this field. Recent advances in vision-centric reasoning explore a promising "Thinking with Images" paradigm for LMMs, marking a shift from image-assisted reasoning to image-interactive thinking. While this milestone enables models to focus on fine-grained image regions, progress remains constrained by limited visual tool spaces and task-specific workflow designs. To bridge this gap, we present V-Thinker, a general-purpose multimodal reasoning assistant that enables interactive, vision-centric thinking through end-to-end reinforcement learning. V-Thinker comprises two key components: (1) a Data Evolution Flywheel that automatically synthesizes, evolves, and verifies interactive reasoning datasets across three dimensions-diversity, quality, and difficulty; and (2) a Visual Progressive Training Curriculum that first aligns perception via point-level supervision, then integrates interactive reasoning through a two-stage reinforcement learning framework. Furthermore, we introduce VTBench, an expert-verified benchmark targeting vision-centric interactive reasoning tasks. Extensive experiments demonstrate that V-Thinker consistently outperforms strong LMM-based baselines in both general and interactive reasoning scenarios, providing valuable insights for advancing image-interactive reasoning applications.
Beyond Shortest Path: Agentic Vehicular Routing with Semantic Context
Venue: arXiv cs.AI 2025
Year: 2025

Traditional vehicle routing systems efficiently optimize singular metrics like time or distance, and when considering multiple metrics, they need more processes to optimize . However, they lack the capability to interpret and integrate the complex, semantic, and dynamic contexts of human drivers, such as multi-step tasks, situational constraints, or urgent needs. This paper introduces and evaluates PAVe (Personalized Agentic Vehicular Routing), a hybrid agentic assistant designed to augment classical pathfinding algorithms with contextual reasoning. Our approach employs a Large Language Model (LLM) agent that operates on a candidate set of routes generated by a multi-objective (time, CO2) Dijkstra algorithm. The agent evaluates these options against user-provided tasks, preferences, and avoidance rules by leveraging a pre-processed geospatial cache of urban Points of Interest (POIs). In a benchmark of realistic urban scenarios, PAVe successfully used complex user intent into appropriate route modifications, achieving over 88% accuracy in its initial route selections with a local model. We conclude that combining classical routing algorithms with an LLM-based semantic reasoning layer is a robust and effective approach for creating personalized, adaptive, and scalable solutions for urban mobility optimization.
Affine Frequency Division Multiplexing: From Communication to Sensing
Venue: arXiv cs.IT 2025
Year: 2025

Affine Frequency Division Multiplexing (AFDM) has been proposed as an effective waveform for achieving the full diversity of doubly-dispersive (delay-Doppler) channels. While this property is closely related to range and velocity estimation in sensing, this article focuses on other AFDM features that are particularly relevant for addressing two challenges in integrated sensing and communication (ISAC) systems: (1) maintaining receiver complexity and energy consumption at acceptable levels while supporting the large bandwidths required for high delay/range resolution, and (2) mitigating interference in multiradar environments. In monostatic sensing, where direct transmitter-receiver leakage is a major impairment, we show that AFDM-based ISAC receivers can address the first challenge through their compatibility with low-complexity self-interference cancellation (SIC) schemes and reduced sampling rates via analog dechirping. In bistatic sensing, where such analog solutions may not be feasible, we demonstrate that AFDM supports sub-Nyquist sampling without requiring hardware modifications while preserving delay resolution. Finally, we show that the second challenge can be addressed by leveraging the resource-assignment flexibility of the discrete affine Fourier transform (DAFT) underlying the AFDM waveform.
Enabling Dynamic Sparsity in Quantized LLM Inference
Venue: arXiv cs.DC 2025
Year: 2025

Deploying large language models (LLMs) on end-user devices is gaining importance due to benefits in responsiveness, privacy, and operational cost. Yet the limited memory and compute capability of mobile and desktop GPUs make efficient execution difficult. Recent observations suggest that the internal activations of LLMs are often dynamically sparse, meaning that for each input, only part of the network contributes significantly to the output. Such sparsity could reduce computation, but it interacts poorly with group-wise quantization, which remains the dominant approach for fitting LLMs onto resource-constrained hardware. To reconcile these two properties, this study proposes a set of techniques that realize dynamic sparse inference under low-bit quantization. The method features: (1) a zigzag-patterned quantization layout that organizes weights in a way consistent with activation sparsity and improves GPU memory locality; (2) a specialized GEMV kernel designed for this layout to fully utilize parallel compute units; and (3) a compact runtime mechanism that gathers sparse indices with minimal overhead. Across several model scales and hardware configurations, the approach achieves up to 1.55x faster decoding throughput while maintaining accuracy comparable to dense quantized inference, showing that structured sparsity and quantization can effectively coexist on commodity GPUs.
Generate, Evaluate, Iterate: Synthetic Data for Human-in-the-Loop Refinement of LLM Judges
Venue: arXiv cs.HC 2025
Year: 2025

The LLM-as-a-judge paradigm enables flexible, user-defined evaluation, but its effectiveness is often limited by the scarcity of diverse, representative data for refining criteria. We present a tool that integrates synthetic data generation into the LLM-as-a-judge workflow, empowering users to create tailored and challenging test cases with configurable domains, personas, lengths, and desired outcomes, including borderline cases. The tool also supports AI-assisted inline editing of existing test cases. To enhance transparency and interpretability, it reveals the prompts and explanations behind each generation. In a user study (N=24), 83% of participants preferred the tool over manually creating or selecting test cases, as it allowed them to rapidly generate diverse synthetic data without additional workload. The generated synthetic data proved as effective as hand-crafted data for both refining evaluation criteria and aligning with human preferences. These findings highlight synthetic data as a promising alternative, particularly in contexts where efficiency and scalability are critical.
Perceptions of AI Bad Behavior: Variations on Discordant Non-Performance
Venue: arXiv cs.HC 2025
Year: 2025

Popular discourses are thick with narratives of generative AI's problematic functions and outcomes, yet there is little understanding of how non-experts consider AI activities to constitute bad behavior. This study starts to bridge that gap through inductive analysis of interviews with non-experts (N = 28) focusing on large-language models in general and their bad behavior, specifically. Results suggest bad behaviors are not especially salient when people discuss AI generally but the notion of AI behaving badly is easily engaged when prompted, and bad behavior becomes even more salient when evaluating specific AI behaviors. Types of observed behaviors considered bad mostly align with their inspiring moral foundations; across all observed behaviors, some variations on non-performance and social discordance were present. By scaffolding findings at the intersections of moral foundations theory, construal level theory, and moral dyadism, a tentative framework for considering AI bad behavior is proposed.
Decoding Emergent Big Five Traits in Large Language Models: Temperature-Dependent Expression and Architectural Clustering
Venue: arXiv cs.CL 2025
Year: 2025

As Large Language Models (LLMs) become integral to human-centered applications, understanding their personality-like behaviors is increasingly important for responsible development and deployment. This paper systematically evaluates six LLMs, applying the Big Five Inventory-2 (BFI-2) framework, to assess trait expressions under varying sampling temperatures. We find significant differences across four of the five personality dimensions, with Neuroticism and Extraversion susceptible to temperature adjustments. Further, hierarchical clustering reveals distinct model clusters, suggesting that architectural features may predispose certain models toward stable trait profiles. Taken together, these results offer new insights into the emergence of personality-like patterns in LLMs and provide a new perspective on model tuning, selection, and the ethical governance of AI systems. We share the data and code for this analysis here: https://osf.io/bsvzc/?view_only=6672219bede24b4e875097426dc3fac1
Comparing EPGP Surrogates and Finite Elements Under Degree-of-Freedom Parity
Venue: arXiv cs.LG 2025
Year: 2025

We present a new benchmarking study comparing a boundary-constrained Ehrenpreis--Palamodov Gaussian Process (B-EPGP) surrogate with a classical finite element method combined with Crank--Nicolson time stepping (CN-FEM) for solving the two-dimensional wave equation with homogeneous Dirichlet boundary conditions. The B-EPGP construction leverages exponential-polynomial bases derived from the characteristic variety to enforce the PDE and boundary conditions exactly and employs penalized least squares to estimate the coefficients. To ensure fairness across paradigms, we introduce a degrees-of-freedom (DoF) matching protocol. Under matched DoF, B-EPGP consistently attains lower space-time $L^2$-error and maximum-in-time $L^{2}$-error in space than CN-FEM, improving accuracy by roughly two orders of magnitude.
A New Probabilistic Mobile Byzantine Failure Model for Self-Protecting Systems
Venue: arXiv cs.DC 2025
Year: 2025

Modern distributed systems face growing security threats, as attackers continuously enhance their skills and vulnerabilities span across the entire system stack, from hardware to the application layer. In the system design phase, fault tolerance techniques can be employed to safeguard systems. From a theoretical perspective, an attacker attempting to compromise a system can be abstracted by considering the presence of Byzantine processes in the system. Although this approach enhances the resilience of the distributed system, it introduces certain limitations regarding the accuracy of the model in reflecting real-world scenarios. In this paper, we consider a self-protecting distributed system based on the \emph{Monitoring-Analyse-Plan-Execute over a shared Knowledge} (MAPE-K) architecture, and we propose a new probabilistic Mobile Byzantine Failure (MBF) that can be plugged into the Analysis component. Our new model captures the dynamics of evolving attacks and can be used to drive the self-protection and reconfiguration strategy. We analyze mathematically the time that it takes until the number of Byzantine nodes crosses given thresholds, or for the system to self-recover back into a safe state, depending on the rates of Byzantine infection spreading \emph{vs.} the rate of self-recovery. We also provide simulation results that illustrate the behavior of the system under such assumptions.
From Model to Breach: Towards Actionable LLM-Generated Vulnerabilities Reporting
Venue: arXiv cs.CL 2025
Year: 2025

As the role of Large Language Models (LLM)-based coding assistants in software development becomes more critical, so does the role of the bugs they generate in the overall cybersecurity landscape. While a number of LLM code security benchmarks have been proposed alongside approaches to improve the security of generated code, it remains unclear to what extent they have impacted widely used coding LLMs. Here, we show that even the latest open-weight models are vulnerable in the earliest reported vulnerability scenarios in a realistic use setting, suggesting that the safety-functionality trade-off has until now prevented effective patching of vulnerabilities. To help address this issue, we introduce a new severity metric that reflects the risk posed by an LLM-generated vulnerability, accounting for vulnerability severity, generation chance, and the formulation of the prompt that induces vulnerable code generation - Prompt Exposure (PE). To encourage the mitigation of the most serious and prevalent vulnerabilities, we use PE to define the Model Exposure (ME) score, which indicates the severity and prevalence of vulnerabilities a model generates.
LLM-as-a-Judge: Toward World Models for Slate Recommendation Systems
Venue: arXiv cs.IR 2025
Year: 2025

Modeling user preferences across domains remains a key challenge in slate recommendation (i.e. recommending an ordered sequence of items) research. We investigate how Large Language Models (LLM) can effectively act as world models of user preferences through pairwise reasoning over slates. We conduct an empirical study involving several LLMs on three tasks spanning different datasets. Our results reveal relationships between task performance and properties of the preference function captured by LLMs, hinting towards areas for improvement and highlighting the potential of LLMs as world models in recommender systems.
ARETE: an R package for Automated REtrieval from TExt with large language models
Venue: arXiv cs.LG 2025
Year: 2025

1. A hard stop for the implementation of rigorous conservation initiatives is our lack of key species data, especially occurrence data. Furthermore, researchers have to contend with an accelerated speed at which new information must be collected and processed due to anthropogenic activity. Publications ranging from scientific papers to gray literature contain this crucial information but their data are often not machine-readable, requiring extensive human work to be retrieved. 2. We present the ARETE R package, an open-source software aiming to automate data extraction of species occurrences powered by large language models, namely using the chatGPT Application Programming Interface. This R package integrates all steps of the data extraction and validation process, from Optical Character Recognition to detection of outliers and output in tabular format. Furthermore, we validate ARETE through systematic comparison between what is modelled and the work of human annotators. 3. We demonstrate the usefulness of the approach by comparing range maps produced using GBIF data and with those automatically extracted for 100 species of spiders. Newly extracted data allowed to expand the known Extent of Occurrence by a mean three orders of magnitude, revealing new areas where the species were found in the past, which mayhave important implications for spatial conservation planning and extinction risk assessments. 4. ARETE allows faster access to hitherto untapped occurrence data, a potential game changer in projects requiring such data. Researchers will be able to better prioritize resources, manually verifying selected species while maintaining automated extraction for the majority. This workflow also allows predicting available bibliographic data during project planning.
The Size of Interpolants in Modal Logics
Venue: arXiv cs.LO 2025
Year: 2025

We start a systematic investigation of the size of Craig interpolants, uniform interpolants, and strongest implicates for (quasi-)normal modal logics. Our main upper bound states that for tabular modal logics, the computation of strongest implicates can be reduced in polynomial time to uniform interpolant computation in classical propositional logic. Hence they are of polynomial dag-size iff NP $\subseteq$ P$_{/\text{poly}}$. The reduction also holds for Craig interpolants and uniform interpolants if the tabular modal logic has the Craig interpolation property. Our main lower bound shows an unconditional exponential lower bound on the size of Craig interpolants and strongest implicates covering almost all non-tabular standard normal modal logics. For normal modal logics contained in or containing S4 or GL we obtain the following dichotomy: tabular logics have ``propositionally sized'' interpolants while for non-tabular logics an unconditional exponential lower bound holds.
Question the Questions: Auditing Representation in Online Deliberative Processes
Venue: arXiv cs.AI 2025
Year: 2025

A central feature of many deliberative processes, such as citizens' assemblies and deliberative polls, is the opportunity for participants to engage directly with experts. While participants are typically invited to propose questions for expert panels, only a limited number can be selected due to time constraints. This raises the challenge of how to choose a small set of questions that best represent the interests of all participants. We introduce an auditing framework for measuring the level of representation provided by a slate of questions, based on the social choice concept known as justified representation (JR). We present the first algorithms for auditing JR in the general utility setting, with our most efficient algorithm achieving a runtime of $O(mn\log n)$, where $n$ is the number of participants and $m$ is the number of proposed questions. We apply our auditing methods to historical deliberations, comparing the representativeness of (a) the actual questions posed to the expert panel (chosen by a moderator), (b) participants' questions chosen via integer linear programming, (c) summary questions generated by large language models (LLMs). Our results highlight both the promise and current limitations of LLMs in supporting deliberative processes. By integrating our methods into an online deliberation platform that has been used for over hundreds of deliberations across more than 50 countries, we make it easy for practitioners to audit and improve representation in future deliberations.
Neural Computation Without Slots: Steps Towards Biologically Plausible Memory and Attention in Natural and Artificial Intelligence
Venue: arXiv cs.NE 2025
Year: 2025

Many models used in artificial intelligence and cognitive science rely on multi-element patterns stored in "slots" - dedicated storage locations - in a digital computer. As biological brains likely lack slots, we consider how they might achieve similar functional outcomes without them by building on the neurally-inspired modern Hopfield network (MHN; Krotov & Hopfield, 2021), which stores patterns in the connection weights of an individual neuron. We propose extensions of this approach to increase its biological plausibility as a model of memory and to capture an important advantage of slot-based computation in contemporary language models. For memory, neuroscience research suggests that the weights of overlapping sparse ensembles of neurons, rather than a dedicated individual neuron, are used to store a memory. We introduce the K-winner MHN, extending the approach to ensembles, and find that within a continual learning regime, the ensemble-based MHN exhibits greater retention of older memories, as measured by the graded sensitivity measure d', than a standard (one-neuron) MHN. Next, we consider the powerful use of slot-based memory in contemporary language models. These models use slots to store long sequences of past inputs and their learned encodings, supporting later predictions and allowing error signals to be transported backward in time to adjust weights underlying the learned encodings of these past inputs. Inspired by these models' successes, we show how the MHN can be extended to capture both of these important functional outcomes. Collectively, our modeling approaches constitute steps towards understanding how biologically plausible mechanisms can support computations that have enabled AI systems to capture human-like abilities that no prior models have been able to achieve.