OpenRaiser/Intern-Atlas huggingface dataset:
PT$^2$-LLM: Post-Training Ternarization for Large Language Models
Venue: arXiv cs.LG 2026
Year: 2026

Large Language Models (LLMs) have shown impressive capabilities across diverse tasks, but their large memory and compute demands hinder deployment. Ternarization has gained attention as a promising compression technique, delivering substantial size reduction and high computational efficiency. However, its potential in the post-training quantization (PTQ) setting remains underexplored, due to the challenge of training-free parameter optimization and the quantization difficulty posed by outliers and dispersed weights. To address these issues, we propose PT$^2$-LLM, a post-training ternarization framework tailored for LLMs. At its core is an Asymmetric Ternary Quantizer equipped with a two-stage refinement pipeline: (1) Iterative Ternary Fitting (ITF), which alternates between optimal ternary grid construction and flexible rounding to minimize quantization error, and (2) Activation-aware Grid Alignment (AGA), which further refines the ternary grid to better match full-precision outputs. In addition, we propose a plug-and-play Structural Similarity-based Reordering (SSR) strategy that leverages inter-column structural similarity to ease quantization and mitigate outlier effects, further enhancing overall performance. Extensive experiments demonstrate that PT$^2$-LLM delivers competitive performance against state-of-the-art (SOTA) 2-bit PTQ methods with lower memory cost, while also accelerating both prefill and decoding to achieve end-to-end speedup. The code and models will be available at https://github.com/XIANGLONGYAN/PT2-LLM.
Where to Add PDE Diffusion in Transformers
Venue: arXiv cs.LG 2026
Year: 2026

Transformers enable powerful content-based global routing via self-attention, but they lack an explicit local geometric prior along the sequence axis. As a result, the placement of locality-inducing modules in hybrid architectures has largely been empirical. We study a simple deterministic PDE diffusion layer implemented as one explicit Euler step of one-dimensional heat smoothing using a discrete Neumann Laplacian under a spectral stability constraint, and ask a structural question: where should diffusion be inserted relative to attention? Our central claim is that diffusion and attention generally do not commute, so inserting the same local operator before versus after attention leads to qualitatively different behaviors. We develop a three-layer operator-theoretic framework that (1) establishes unconditional guarantees for the diffusion subsystem, including spectral non-expansiveness and monotone Dirichlet-energy dissipation when the diffusion step size is smaller than one half, (2) derives compositional perturbation bounds linking insertion effects to representation roughness and downstream amplification, and (3) uses diffusion-attention non-commutativity as a diagnostic for structural double-mixing conflicts. Guided by theory, we evaluate seven insertion positions on the Long Range Arena benchmark. Early diffusion acts as effective pre-regularization, improving average accuracy by 4.1 percentage points when applied after embedding, while post-attention diffusion degrades performance by 2.5 percentage points, consistent with the predicted conflict. A multi-scale diffusion variant yields consistent gains under the same global stability constraint. Our analysis provides a general template for reasoning about local-global compositions in sequence models by separating provable guarantees, compositional bounds, and mechanistic diagnostics.
Universal Beta Splatting
Venue: arXiv cs.GR 2026
Year: 2026

We introduce Universal Beta Splatting (UBS), a unified framework that generalizes 3D Gaussian Splatting to N-dimensional anisotropic Beta kernels for explicit radiance field rendering. Unlike fixed Gaussian primitives, Beta kernels enable controllable dependency modeling across spatial, angular, and temporal dimensions within a single representation. Our unified approach captures complex light transport effects, handles anisotropic view-dependent appearance, and models scene dynamics without requiring auxiliary networks or specific color encodings. UBS maintains backward compatibility by approximating to Gaussian Splatting as a special case, guaranteeing plug-in usability and lower performance bounds. The learned Beta parameters naturally decompose scene properties into interpretable without explicit supervision: spatial (surface vs. texture), angular (diffuse vs. specular), and temporal (static vs. dynamic). Our CUDA-accelerated implementation achieves real-time rendering while consistently outperforming existing methods across static, view-dependent, and dynamic benchmarks, establishing Beta kernels as a scalable universal primitive for radiance field rendering. Our project website is available at https://rongliu-leo.github.io/universal-beta-splatting/.
Disentangling Recall and Reasoning in Transformer Models through Layer-wise Attention and Activation Analysis
Venue: arXiv cs.LG 2026
Year: 2026

Transformer-based language models excel at both recall (retrieving memorized facts) and reasoning (performing multi-step inference), but whether these abilities rely on distinct internal mechanisms remains unclear. Distinguishing recall from reasoning is crucial for predicting model generalization, designing targeted evaluations, and building safer interventions that affect one ability without disrupting the other.We approach this question through mechanistic interpretability, using controlled datasets of synthetic linguistic puzzles to probe transformer models at the layer, head, and neuron level. Our pipeline combines activation patching and structured ablations to causally measure component contributions to each task type. Across two model families (Qwen and LLaMA), we find that interventions on distinct layers and attention heads lead to selective impairments: disabling identified "recall circuits" reduces fact-retrieval accuracy by up to 15\% while leaving reasoning intact, whereas disabling "reasoning circuits" reduces multi-step inference by a comparable margin. At the neuron level, we observe task-specific firing patterns, though these effects are less robust, consistent with neuronal polysemanticity.Our results provide the first causal evidence that recall and reasoning rely on separable but interacting circuits in transformer models. These findings advance mechanistic interpretability by linking circuit-level structure to functional specialization and demonstrate how controlled datasets and causal interventions can yield mechanistic insights into model cognition, informing safer deployment of large language models.
Connectivity Maintenance and Recovery for Multi-Robot Motion Planning
Venue: arXiv cs.RO 2026
Year: 2026

Connectivity is crucial in many multi-robot applications, yet balancing between maintaining it and the fleet's traversability in obstacle-rich environments remains a challenge. Reactive controllers, such as control barrier functions, while providing connectivity guarantees, often struggle to traverse obstacle-rich environments due to deadlocks. We propose a real-time Bézier-based constrained motion planning algorithm, namely, MPC--CLF--CBF, that produces trajectory and control concurrently, under high-order control barrier functions and control Lyapunov functions conditions. Our motion planner significantly improves the navigation success rate of connected fleets in a cluttered workspace and recovers after inevitable connection loss by bypassing obstacles or from an initially disconnected fleet configuration. In addition, our predictive motion planner, owing to its Bézier curve solution, can easily obtain continuous-time arbitrary orders of derivatives, making it suitable for agile differentially flat systems, such as quadrotors. We validate the proposed algorithm through simulations and a physical experiment with $8$ Crazyflie nano-quadrotors.
Optimal Scaling Needs Optimal Norm
Venue: arXiv cs.LG 2026
Year: 2026

Despite recent progress in optimal hyperparameter transfer under model and dataset scaling, no unifying explanatory principle has been established. For Adam and Scion optimizers, we discover that joint optimal scaling across model and dataset sizes is conditioned on a single invariant: the operator norm of the output layer. Across models with up to 1.3B parameters trained on up to 138B tokens, the optimal learning rate/batch size pair $(η^{\ast}, B^{\ast})$ consistently has the same operator norm value - a phenomenon we term norm transfer. This constant norm condition is necessary but not sufficient: while for each dataset size, multiple $(η, B)$ reach the optimal norm, only a unique $(η^{\ast}, B^{\ast})$ achieves the best loss. As a sufficient condition, we provide the first measurement of $(η^{\ast}, B^{\ast})$ scaling with dataset size for Scion, and find that the scaling rules are consistent with those of Adam. Tuning per-layer-group learning rates also improves model performance, with the output layer being the most sensitive and hidden layers benefiting from lower learning rates. We provide practical insights on norm-guided optimal scaling and release our Distributed Scion (Disco) implementation with logs from over two thousand runs to support research on LLM training dynamics at scale.
TRACE: AI-Assisted Assessment of Collaborative Projects in Computer Science Education
Venue: arXiv cs.HC 2026
Year: 2026

Collaborative group projects are integral to computer science education, fostering teamwork, problem-solving, and industry-relevant skills. However, assessing individual contributions within group settings remains challenging. Traditional approaches, including equal grade distribution and subjective peer evaluations, often lack fairness, objectivity, and scalability, particularly in large classrooms. We propose TRACE, a semi-automated AI-assisted framework for assessing collaborative software projects that evaluates both project quality and individual contributions using repository mining, communication analytics, and AI-assisted analytics. A pilot deployment in a software engineering course demonstrated high alignment with instructor assessments, increased student satisfaction, and reduced instructor grading effort. The results suggest that AI-assisted analytics can improve the transparency and scalability of collaborative project assessment in computer science education.
The LCLStream Ecosystem for Multi-Institutional Dataset Exploration
Venue: arXiv cs.IR 2026
Year: 2026

We describe a new end-to-end experimental data streaming framework designed from the ground up to support new types of applications -- AI training, extremely high-rate X-ray time-of-flight analysis, crystal structure determination with distributed processing, and custom data science applications and visualizers yet to be created. Throughout, we use design choices merging cloud microservices with traditional HPC batch execution models for security and flexibility. This project makes a unique contribution to the DOE Integrated Research Infrastructure (IRI) landscape. By creating a flexible, API-driven data request service, we address a significant need for high-speed data streaming sources for the X-ray science data analysis community. With the combination of data request API, mutual authentication web security framework, job queue system, high-rate data buffer, and complementary nature to facility infrastructure, the LCLStreamer framework has prototyped and implemented several new paradigms critical for future generation experiments.
GDiffuSE: Diffusion-based speech enhancement with noise model guidance
Venue: arXiv cs.SD 2026
Year: 2026

This paper introduces a novel speech enhancement (SE) approach based on a denoising diffusion probabilistic model (DDPM), termed Guided diffusion for speech enhancement (GDiffuSE). In contrast to conventional methods that directly map noisy speech to clean speech, our method employs a lightweight helper model to estimate the noise distribution, which is then incorporated into the diffusion denoising process via a guidance mechanism. This design improves robustness by enabling seamless adaptation to unseen noise types and by leveraging large-scale DDPMs originally trained for speech generation in the context of SE. We evaluate our approach on noisy signals obtained by adding noise samples from the BBC sound effects database to LibriSpeech utterances, showing consistent improvements over state-of-the-art baselines under mismatched noise conditions. Examples are available at our project webpage.
Cooperative Flexibility Exchange: Fair and Comfort-Aware Decentralized Resource Allocation
Venue: arXiv cs.MA 2026
Year: 2026

The growing electricity demand and use of smart appliances are placing pressure on power grids, making efficient energy management more important than ever. The existing energy management systems often prioritize system efficiency (balanced energy demand and supply) at the expense of consumer comfort. This paper addresses this gap by proposing a novel decentralized multi-agent coordination-based demand-side management system. The proposed system enables individual agents to coordinate for demand-side energy optimization while improving consumer comfort and maintaining system efficiency. A key innovation of this work is the introduction of a slot exchange mechanism, where agents first receive optimized appliance-level energy consumption schedules and then coordinate with each other to adjust these schedules through slot exchanges to improve their comfort even when agents show non-altruistic behaviour. It also scales well with large populations and promotes fairness by balancing satisfaction levels across consumers. For performance evaluation, a real-world dataset is used, and the results demonstrate that the proposed slot exchange mechanism increases consumer comfort and fairness without raising system inefficiency cost, making it a practical and scalable solution for future smart grids.
JEF-Hinter: Leveraging Offline Knowledge for Improving Web Agents Adaptation
Venue: arXiv cs.AI 2026
Year: 2026

Large language model (LLM) agents perform well in sequential decision-making tasks, but improving them on unfamiliar domains often requires costly online interactions or fine-tuning on large expert datasets. These strategies are impractical for closed-source models and expensive for open-source ones, with risks of catastrophic forgetting. Offline trajectories offer reusable knowledge, yet demonstration-based methods struggle because raw traces are long, noisy, and tied to specific tasks. We present Just-in-time Episodic Feedback Hinter (JEF-Hinter), an agentic system that distills offline traces into compact, context-aware hints. A zooming mechanism highlights decisive steps in long trajectories, capturing both strategies and pitfalls. Unlike prior methods, JEF-Hinter leverages both successful and failed trajectories, extracting guidance even when only failure data is available, while supporting parallelized hint generation and benchmark-independent prompting. At inference, a retriever selects relevant hints for the current state, providing targeted guidance with transparency and traceability. Experiments on MiniWoB++, WorkArena-L1, and WebArena-Lite show that JEF-Hinter consistently outperforms strong baselines, including human- and document-based hints.
Scale-Invariant Regret Matching and Online Learning with Optimal Convergence: Bridging Theory and Practice in Zero-Sum Games
Venue: arXiv cs.GT 2026
Year: 2026

A considerable chasm has been looming for decades between theory and practice in zero-sum game solving through first-order methods. Although a convergence rate of $T^{-1}$ has long been established, the most effective paradigm in practice is counterfactual regret minimization (CFR), which is based on regret matching and its modern variants. In particular, the state of the art across most benchmarks is predictive regret matching$^+$ (PRM$^+$). Yet, such algorithms can exhibit slower $T^{-1/2}$ convergence even in self-play. In this paper, we close the gap between theory and practice. We propose a new scale-invariant and parameter-free variant of PRM$^+$, which we call IREG-PRM$^+$. We show that it achieves $T^{-1/2}$ best-iterate and $T^{-1}$ (i.e., optimal) average-iterate convergence guarantees, while also being on par or even better relative to PRM$^+$ on benchmark games. From a technical standpoint, we draw an analogy between (IREG-)PRM$^+$ and optimistic gradient descent with adaptive learning rate. Reflecting this theoretical bridge, we find that the adaptive version of optimistic gradient descent we consider performs on par with IREG-PRM$^+$. This demystifies the effectiveness of the regret matching family vis-a-vis more standard optimization techniques. Moreover, we extend our analysis beyond zero-sum games to a family of variational inequality problems that includes harmonic games, as well as extensive-form games with fully-mixed equilibria, via a new and intriguing connection between CFR and harmonic games. Unlike prior work in harmonic games, our algorithms do not require knowing the underlying weights by virtue of scale invariance. Under the weighted Minty condition, we show that any algorithm satisfying a scale-invariant RVU property (such as IREG-PRM$^+$) has constant regret (in self-play) and $T^{-1/2}$ iterate convergence.
FourierCSP: Differentiable Constraint Satisfaction Problem Solving by Walsh-Fourier Expansion
Venue: arXiv cs.AI 2026
Year: 2026

The Constraint-satisfaction problem (CSP) is fundamental in mathematics, physics, and theoretical computer science. Continuous local search (CLS) solvers, as recent advancements, can achieve highly competitive results on certain classes of Boolean satisfiability (SAT) problems. Motivated by these advances, we extend the CLS framework from Boolean SAT to general CSP with finite-domain variables and expressive constraint formulations. We present FourierCSP, a continuous optimization framework that generalizes the Walsh-Fourier transform to CSP, allowing for transforming versatile constraints to compact multilinear polynomials, thereby avoiding the need for auxiliary variables and memory-intensive encodings. We employ projected subgradient and mirror descent algorithms with provable convergence guarantees, and further combine them to accelerate gradient-based optimization. Empirical results on benchmark suites demonstrate that FourierCSP is scalable and competitive, significantly broadening the class of problems that can be efficiently solved by differentiable CLS techniques and paving the way toward end-to-end neurosymbolic integration.
ChartAgent: A Multimodal Agent for Visually Grounded Reasoning in Complex Chart Question Answering
Venue: arXiv cs.AI 2026
Year: 2026

Recent multimodal LLMs have shown promise in chart-based visual question answering, but their performance declines sharply on unannotated charts-those requiring precise visual interpretation rather than relying on textual shortcuts. To address this, we introduce ChartAgent, a novel agentic framework that explicitly performs visual reasoning directly within the chart's spatial domain. Unlike textual chain-of-thought reasoning, ChartAgent iteratively decomposes queries into visual subtasks and actively manipulates and interacts with chart images through specialized actions such as drawing annotations, cropping regions (e.g., segmenting pie slices, isolating bars), and localizing axes, using a library of chart-specific vision tools to fulfill each subtask. This iterative reasoning process closely mirrors human cognitive strategies for chart comprehension. ChartAgent achieves state-of-the-art accuracy on the ChartBench and ChartX benchmarks, surpassing prior methods by up to 16.07% absolute gain overall and 17.31% on unannotated, numerically intensive queries. Furthermore, our analyses show that ChartAgent is (a) effective across diverse chart types, (b) achieves the highest scores across varying visual and reasoning complexity levels, and (c) serves as a plug-and-play framework that boosts performance across diverse underlying LLMs. Our work is among the first to demonstrate visually grounded reasoning for chart understanding using tool-augmented multimodal agents.
Busemann Functions in the Wasserstein Space: Existence, Closed-Forms, and Applications to Slicing
Venue: arXiv cs.LG 2026
Year: 2026

The Busemann function has recently found much interest in a variety of geometric machine learning problems, as it naturally defines projections onto geodesic rays of Riemannian manifolds and generalizes the notion of hyperplanes. As several sources of data can be conveniently modeled as probability distributions, it is natural to study this function in the Wasserstein space, which carries a rich formal Riemannian structure induced by Optimal Transport metrics. In this work, we investigate the existence and computation of Busemann functions in Wasserstein space, which admits geodesic rays. We establish closed-form expressions in two important cases: one-dimensional distributions and Gaussian measures. These results enable explicit projection schemes for probability distributions on $\mathbb{R}$, which in turn allow us to define novel Sliced-Wasserstein distances over Gaussian mixtures and labeled datasets. We demonstrate the efficiency of those original schemes on synthetic datasets as well as transfer learning problems.
Directional Sheaf Hypergraph Networks: Unifying Learning on Directed and Undirected Hypergraphs
Venue: arXiv cs.LG 2026
Year: 2026

Hypergraphs provide a natural way to represent higher-order interactions among multiple entities. While undirected hypergraphs have been extensively studied, the case of directed hypergraphs, which can model oriented group interactions, remains largely under-explored despite its relevance for many applications. Recent approaches in this direction often exhibit an implicit bias toward homophily, which limits their effectiveness in heterophilic settings. Rooted in the algebraic topology notion of Cellular Sheaves, Sheaf Neural Networks (SNNs) were introduced as an effective solution to circumvent such a drawback. While a generalization to hypergraphs is known, it is only suitable for undirected hypergraphs, failing to tackle the directed case. In this work, we introduce Directional Sheaf Hypergraph Networks (DSHN), a framework integrating sheaf theory with a principled treatment of asymmetric relations within a hypergraph. From it, we construct the Directed Sheaf Hypergraph Laplacian, a complex-valued operator by which we unify and generalize many existing Laplacian matrices proposed in the graph- and hypergraph-learning literature. Across 7 real-world datasets and against 13 baselines, DSHN achieves relative accuracy gains from 2% up to 20%, showing how a principled treatment of directionality in hypergraphs, combined with the expressive power of sheaves, can substantially improve performance.
What Do We Mean When We Talk About Data Storytelling?
Venue: arXiv cs.HC 2026
Year: 2026

Data storytelling has seen rapid growth through a proliferation of examples, as well as theoretical and technical advancements contributed across multiple disciplines. In this paper, we present a comprehensive survey of data storytelling research from 2010 to 2025. By analyzing the conceptualizations of data storytelling collected from related publications, we reveal the field's perspectives on the What, How, Why, and Who of data storytelling. We further investigated the operationalization of data stories. We identified 12 data story forms that provide concrete examples of how data stories have been presented. We derived a set of spectrum-based dimensions that capture important properties of data stories. Along each spectrum, applicable forms and design alternatives were discussed to analyze how they shape data storytelling experiences, along with data storytelling design trade-offs. Additionally, we examine how traditional narrative elements, like plot and character, have been adapted in data stories to support the operationalization of a data storytelling narratological perspective. Finally, we concluded the survey with a synthesis of our major findings and implications for future research.