Artificial Intelligence
Analyzing the Dynamics of WSBM-based Echo State Networks using InputDSA
Huy Mai and Robert Clarke
Bridging the structural gap between biological
brains and artificial neural networks presents a pathway to altered computational dynamics. This paper introduces a Reservoir Computing framework that replaces random networks with biologically realistic modularity by applying the Weighted
Stochastic Block Model (WSBM) to Echo State Networks (ESNs). This topological parameterization constructs reservoirs reflecting latent functional sub-
circuits. Furthermore, the integration of Input Dynamical Similarity Analysis (InputDSA) provides a mechanism to isolate and compare how structural priors modulate intrinsic recurrent dynamics versus external inputs. By evaluating these architectures, we establish that structural diversity does not necessarily predicts behavioral diversity across the selected network subset. Furthermore, our demixed analysis confirms Anna Karenina principle of dynamics, demonstrating that environmental constraints enforce dynamical convergence in successful
agents, while failing networks degenerate into idiosyncratic trajectories.
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Artificial Intelligence · Cross-listed from cs.CL
In-Place Indic Tokenizer Expansion and Continual Pretraining of Nemotron-3-Nano-30B-A3B
Avinash Benki, Prasanjith Pasunuti, Sutanshu Saksena Raj et al. (+3 more)
We adapted Nemotron-3-Nano-30B-A3B for eight Indian
languages without changing its backbone architecture. We expanded the vocabulary in place from 131,072 to 163,072 entries using continued byte-pair encoding and AdaptMix rebalancing, reducing
tokens per word by 38.6%. We initialized new input and output rows by averaging their original constituent rows, then trained them on 90% Indic and 10% English text before updating the backbone. Continual pretraining then progressed through broad training, high-quality annealing, and long-context training over approximately 1.27 trillion
tokens. The annealed checkpoint gained 12.9 points on the Indic MILU
benchmark but lost 3.4 on English MMLU. We also report practical negative results. Direct
token addition underperformed continued byte-pair encoding. AdamW weight decay changed rows intended to stay fixed. Perplexity on an Indic-heavy holdout did not select the learning rate that best preserved English performance.
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All authors Avinash Benki, Prasanjith Pasunuti, Sutanshu Saksena Raj, Parijat Chatterjee, Nayanjit Sarkar, Daksh Maan
Artificial Intelligence
Fewer Bits, More Tries: Recovering Capability Under Memory Constraints
Tomer Galanti, Josh Susskind and Eran Malach
Large
language models are generally more capable than smaller ones, but their
memory requirements can make local deployment difficult. We ask whether part of this capability gap can instead be recovered by running a smaller or more compressed model multiple times and selecting among its outputs using public or model-generated tests. Single-user local inference can leave hardware underutilized, allowing parallel candidate generation to trade additional test-time computation for accuracy at modest latency cost. Across four code-generation
benchmarks, verified retry recovers substantial accuracy lost to model size or compression. A 7B Qwen model with eight parallel attempts essentially matches a 32B model on MBPP+ and exceeds it on the other three
benchmarks, while using about $4\times$ less model
memory, running up to $6\times$ faster, and using less energy, as measured on Nvidia GPU. We observe similar tradeoff across model sizes, compression levels and hardware choices, although exact benefit depends on the hardware profile and problem setting. These results show that model capacity can be traded for verified test-time computation when correct solutions remain reachable within a modest retry budget.
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Artificial Intelligence · Cross-listed from cs.CL
A Sovereign, Indic-First, Reasoning Model
Sutanshu Saksena Raj, Prasanjith Pasunuti, Parijat Chatterjee et al. (+4 more)
Gnani-Evon-v3.3-30B-A3B is a long-context hybrid Mamba-
Transformer mixture-of-experts model for
reasoning, instruction following, and tool use in English and eight Indian
languages. Starting from NVIDIA's Nemotron-3-Nano-30B-A3B checkpoint, it retains the 52-layer backbone and changes the vocabulary, training data, and post-training objectives. An in-place expansion from 131,072 to 163,072
tokens reduces mean Indian-
language fertility by 38.6%. The new
embedding rows are initialized from their upstream decompositions and warmed before the backbone is updated. Continual pretraining then adds broad English-Indic data, high-quality annealing, and direct training at a 131,072-
token sequence length. The central result appears after post-training. Reinforcement learning across four verifiable task environments improves mathematics, graduate-level science, and instruction following, but degrades Indic extractive question answering because the rewards do not require an answer in the requested
language. The share of Indic extraction items answered in English rises from 8.7% to 91.7%. Accuracy conditional on answering in the target script roughly doubles, which is consistent with preserved reading ability but is measured on a self-selected subset. Two unreleased studies test repairs. A scoped Indic reward restores the
language convention. A 26B-A4B generative judge outperforms a 235B-parameter model on RM-Bench, and
distillation restores 81% of the capabilities displaced by optimizing against it. Relative to the architecture-identical starting checkpoint, the released model gains 5.2 points on an Indic aggregate while giving up 1.7 points of English retention. The unreleased Indic-aware branch trades further, gaining 14.6 Indic points at a cost of 5.4 in English. A planned FP8 release reduces the artifact to 32GB with near-zero mean change across 32 paired
benchmarks.
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All authors Sutanshu Saksena Raj, Prasanjith Pasunuti, Parijat Chatterjee, Daksh Maan, Purusharth Malik, Nayanjit Sarkar, Avinash Benki
Artificial Intelligence
TRACE: Temporal Risk Assessment and Concept Editing for Safer Language Generation
Ankur Garg, Xuemin Yu, Hassan Sajjad et al. (+1 more)
Large
language models (LLMs) can produce harmful content while generating a response. A useful safeguard should not only detect this risk during generation but also intervene while leaving safe responses largely unchanged and provide an auditable record of the internal patterns supporting its decisions. We propose TRACE (Temporal Risk Assessment and Concept Editing), a framework that tracks harmful-response risk during generation, selectively edits
token activations, and leaves an auditable trace of its detection and steering decisions. TRACE learns a codebook of recurring activation patterns, with each codebook vector representing a latent concept. At inference time, it assigns each
token activation to the nearest codebook vector to obtain the corresponding concept. A gated recurrent unit (GRU) processes the activations and their assigned concept features to predict completed-response harmfulness and a monotone cumulative streaming risk. During steering, activations assigned to harmful concepts are steered along directions defined by nearby benign targets. An external LLM labels the learned concepts post hoc, making detection sequences and harmful-to-benign edit records interpretable. Experiments across three generators and the WildGuard and S-Eval
benchmarks show that TRACE improves streaming F1 over Kelp in five of six comparisons while remaining competitive on completed-response detection. TRACE's selective steering reduces Harmbench attack success with little change in refusal on originally safe responses. The post-hoc audit reveals recurring semantic structure in the learned concepts and distinct traces associated with harmful detection, safe generation, and activation edits.
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All authors Ankur Garg, Xuemin Yu, Hassan Sajjad, Samira Ebrahimi Kahou
Artificial Intelligence · Cross-listed from cs.LG
Adversarial Detection for Pre-trained Classifiers via Lipschitz-Margin Analysis with Certification
Rui Zhang, Xinle Cao, Huazhen Yu et al. (+2 more)
Deep classifiers deployed in safety-critical services like autonomous-driving perception remain vulnerable to small
adversarial perturbations. Certified $\ell_2$ defenses today face a costly trade-off: randomized smoothing (RS) requires hundreds of noisy queries per input, while Lipschitz networks (LN) constrain the architecture and sacrifice clean accuracy. We take a detection-oriented approach instead: rather than certifying that predictions are invariant inside an $\ell_2$ ball, we certify that
adversarial inputs within an $\ell_2$ ball are detected — a guarantee aligned with security-critical deployments where missing an attack is costlier than flagging a clean input. To realize this, we prove a Lipschitz-margin reduction from certified $\ell_2$ robustness to certified detection, which reduces the problem to handling a single Lipschitz constant $L$. We introduce two detector families: $\mathbb{D}_{Adv}$, which estimates $L$ from a single defender-side perturbed pair, and $\mathbb{D}_{lc}$, which augments that pair with a local margin centroid to keep the estimate above what the certificate requires. Both deploy on any pretrained classifier without retraining. With a valid global bound, the detector is fully certified at that radius; with a local estimate $L'$, certification is sample-wise and applies only when our certified condition holds. Across MNIST, CIFAR-10, and ImageNet-1k on four backbones (LeNet-5, VGG16, ResNet-50, ViT-B/16), our detectors match or outperform five baselines (CTT, SortNet, CRM, ContraNet, A$^2$D). For CIFAR-10 with 1-Lipschitz SortNet, our reduction delivers $100\%$ certified detection at the lowest FPR among compared certified detectors; on the standard CIFAR-10 VGG16, our detectors reject all evaluated in-budget
adversarial examples, with 62-82% both rejected and formally certified.
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All authors Rui Zhang, Xinle Cao, Huazhen Yu, Jian Liu, Youwen Zhu
Artificial Intelligence · Cross-listed from cs.LG
Siam-ALS: An Explainable Siamese Deep Learning Framework for Early ALS Detection Using EEG and Eye-Tracking Signals
Abdur Rahman, A. A. M. Muzahid, Istiak Ahmed et al. (+4 more)
Amyotrophic lateral sclerosis (ALS) diagnosis remains challenging due to heterogeneous symptoms and reliance on clinical assessment, underscoring the need for objective, data-driven approaches that leverage patient-generated physiological signals. We propose Siam-ALS, an explainable Siamese deep learning framework that jointly models synchronized electroencephalography (
EEG) and eye-tracking (ET) signals.
EEG captures ALS-related changes in
brain activity, while ET provides information about oculomotor control, allowing the model to learn complementary patterns associated with ALS. The framework employs two modality-specific encoders: a convolutional
EEG encoder that captures spatiotemporal neural patterns and an MLP-based ET encoder that models oculomotor dynamics. These modality-specific features are processed through weight-sharing Siamese branches and fused into a compact 64-dimensional joint
embedding. A lightweight classifier is used to predict ALS, while pairwise metric learning helps the model learn more effective patterns from the small, diverse ALS
datasets. Performance was evaluated on the public EEGET-ALS
dataset using strict subject-level cross-validation, where Siam-ALS achieved an accuracy of 0.988 and an AUROC of 0.998, demonstrating strong performance compared with classical machine learning, DL, and non-Siamese fusion approaches. Cross-
dataset evaluation on the independent BigP3BCI
dataset achieved an AUROC of approximately 0.98, supporting the generalization capability of Siam-ALS across different BCI paradigms. To enhance interpretability, six XAI methods were evaluated in the learned
embedding space, with SHAP achieving the highest, though statistically comparable, fidelity in identifying important
EEG and ET patterns that contribute to ALS predictions. Overall, Siam-ALS provides an accurate, generalizable, and explainable framework for
multimodal ALS detection.
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All authors Abdur Rahman, A. A. M. Muzahid, Istiak Ahmed, Dipto Biswas, Hua Han, Yujin Zhang, Ferdous Sohel
Artificial Intelligence · Cross-listed from cs.SD
Gugak-VocalSet: A Multi-Genre Studio-Quality Dataset of Korean Traditional Singing with Sigimsae Annotations
Danbinaerin Han, Wonil Kim, Seah Hong et al. (+5 more)
We present Gugak-VocalSet, the first publicly available audio
dataset for Korean traditional singing with dense annotations of sigimsae, a structured pitch contour system of vocal expressions. Gugak-VocalSet contains 24.5 hours of unaccompanied solo vocal recordings across 305 tracks of representative repertoire curated by the National Gugak Center, performed by 20 professional singers in a controlled studio environment with fixed key and tempo. In addition to genre, lyrics, and mood/timbre tags, the
dataset provides a unified sigimsae ontology and approximately 68,000 segments (71,700 instances including multi-label) of audio-grounded sigimsae annotations; the ontology adopts a functional-position-based hierarchy designed for consistent application across genres, and 14 expert annotators specializing in Korean traditional music performed segment-level labeling accordingly. Baseline experiments on sigimsae classification and event detection show that domain-informed F0 representations remain competitive with, and on classification surpass, frozen probing of a general-purpose music foundation model, while pitch and spectral cues play complementary roles in event-level detection. Companion page: https://www.danbinaerin.com/Gugak-VocalSet/
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All authors Danbinaerin Han, Wonil Kim, Seah Hong, Dongjoo Moon, Jaeok Lee, Jongpil Lee, Chaewon Kim, Juhan Nam
Artificial Intelligence · Cross-listed from cs.LG
CodeWalk: Generating Coding Benchmarks by Walking a Problem Graph
Jiaxin Fang, Runyuan He, Arnav Gupta et al. (+3 more)
High-quality coding problems are expensive to collect.
Benchmarks such as SWE-bench rely on substantial human curation, while synthetic alternatives typically rely on costly model generation and validation. In this work, we show that new coding problems can be created at almost no cost by composing existing ones. We formulate problem generation as code composition: verified atomic problems are combined into level-$N$ tasks, and a task is kept only if every atomic problem remains unsolved and a joint reference solution passes the same check used at evaluation time. The entire task-construction process is programmatic and requires no
language-model generation or validation. We instantiate this approach in four coding settings: multi-bug repair, proof completion, complexity analysis, and clone equivalence. The generated tasks are challenging for frontier models, which all degrade as compositional depth increases. The generated tasks are also useful as training data: rejection-sampling fine-tuning on them improves Qwen3.5-9B at every composition level.
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All authors Jiaxin Fang, Runyuan He, Arnav Gupta, Sahil Kumawat, Huanzhi Mao, Alvin Cheung
Artificial Intelligence · Cross-listed from cs.CV
Across Viewpoints and Time: Trajectory-Generalizable Physical Attacks on World Models and Video Understanding Models
Song Xia, Meiwen Ding, Chenqi Kong et al. (+2 more)
World models and
video understanding models are increasingly deployed in dynamic visual environments, yet their robustness to physical
adversarial threats remains largely underexplored. We study whether a single physical
adversarial texture can mislead the victim model across different real-world scenarios where observing viewpoints vary along uncontrolled object–camera trajectories. We identify that heterogeneous
video semantics and trajectories induce poorly aligned gradients for optimizing the
adversarial texture. To address that, we propose the Trajectory-aware
adversarial Alignment and Refinement Attack (TARA), which incorporates trajectory-aware optimization to capture the evolving
geometric variations of a printed texture under real-world scenarios. To improve cross-instance optimization compatibility, TARA incorporates trajectory-aware contrastive
alignment to encourage attacked representations from diverse
video–trajectory instances to approach a shared
adversarial region and then performs trajectory-aware task refinement to translate the aligned representation into task-level prediction errors. For reproducible evaluation, we develop a geometry-grounded tracking pipeline that provides physically plausible
adversarial regions with temporally consistent trajectories on EPIC-KITCHENS-100 and Something-Something V2. We further collect real-world print-and-recapture
videos to validate attack effectiveness under physical deployment conditions. Together, the annotated
benchmarks and real-world captured data form a reproducible testbed for future evaluation. Notably, our results reveal that a single printable
adversarial texture can persistently disrupt world models and
video understanding models under physical conditions, highlighting the fundamental security challenge.
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All authors Song Xia, Meiwen Ding, Chenqi Kong, Wenhan Yang, Xudong Jiang
Artificial Intelligence · Cross-listed from cs.CV
StimFlow: Synthesizing Brain Activity from Images via Stimulus-conditioned Flow Matching
Bui Van Hung, Dang Thanh Vu, Hoang-Son Vo et al. (+3 more)
The inherent variability of fMRI responses across trials encourages a generative view of the stimulus-to-response mapping. Prior generative encoders often operate in compressed latent spaces and condition predominantly on semantic features, which provide limited low-level cues for early visual cortex. We propose StimFlow, which synthesizes fMRI directly in native voxel space using conditional flow matching with a linear path. This design aligns training and inference distributions, enabling competitive performance with a single sample and reducing reliance on averaging. To match the visual hierarchy, StimFlow combines structural DINOv2 features with semantic CLIP features, which prove complementary along the cortical processing stream. With a single sample, StimFlow achieves a per-voxel Pearson correlation of 0.371, matching baselines that average five draws. Generation in voxel space allows direct ROI analysis and concept localization without a decoder. Furthermore, it recovers over half of full-data performance from just one hour of scans, supporting efficient cross-subject adaptation.
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All authors Bui Van Hung, Dang Thanh Vu, Hoang-Son Vo, Mai Duc Minh Huy, Mai Tien Dung, Soo-Hyung Kim
Artificial Intelligence
MultiFlow: Vision-Driven Multimodal fMRI Encoding via Stimulus-Informed Source Flow Matching
Bui Van Hung, Hoang-Son Vo, Mai Duc Minh Huy et al. (+2 more)
Current fMRI encoding models frame prediction as deterministic regression, yet identical stimuli evoke a distribution of neural responses (arising from physiological noise, hemodynamic variability, and cognitive fluctuations) that a single point estimate cannot capture. This work introduces MultiFlow, a generative fMRI encoding framework based on Conditional Flow Matching (CFM) and a formulation termed Stimulus-Informed Source Flow Matching (SISFM). SISFM generalises CFM by conditioning the source distribution on the stimulus: a learnable temporal convolution shapes the source mean with an HRF-length receptive field, while a variance head shrinks its scale, shortening the expected transport path. SISFM is instantiated with a
Diffusion Transformer (DiT-X) decoder parameterising the velocity field, with sub-layer
Attention Residuals, heterogeneous fusion of ten frozen
vision, audio,
language, and audiovisual backbones, and per-subject Yeo-network heads. On the Algonauts 2025
dataset, a single MultiFlow checkpoint recovers most of the benefit of large deterministic ensembles: it trails the 1000-model TRIBE ensemble in-distribution by 0.003 PCC (0.3163 vs. 0.3195), and the strongest in- and out-of-distribution ensembles (VIBE-20, MedARC-50) by 0.010 and 0.009 PCC, using a single trained model rather than an ensemble of 20-1000. The same checkpoint also provides stimulus-conditioned uncertainty estimates and sample-based diversity.
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All authors Bui Van Hung, Hoang-Son Vo, Mai Duc Minh Huy, Dang Thanh Vu, Soo-Hyung Kim
Artificial Intelligence · Cross-listed from cs.LG
ZOIF: Zeroth-Order Influence Function Estimation for LLM Data Selection Using Only Forward Passes
Lisong He, Yunxuan Jiang, Haishan Ye et al. (+1 more)
Influence functions enable principled data selection for large
language models (e.g., identifying influential training samples for model editing, debugging, or efficient fine-tuning). However, scaling IFs to LLMs is hindered by the intractability of billion-scale Hessian
matrices and the prohibitive
memory overhead of backpropagation (BP). While many classical IF approximations reduce Hessian complexity, they still rely on BP for gradient computation, requiring massive storage for intermediate activations that impedes practical data selection at scale. To address this, we propose ZOIF, a zeroth-order framework for influence estimation that operates entirely via forward passes, enabling
memory-efficient data selection under resource constraints. ZOIF decomposes influence estimation into three BP-free stages: (1) approximating the average proxy gradient via zeroth-order estimation; (2) reformulating and solving the inverse Hessian-vector product as an auxiliary zeroth-order optimization to bypass explicit inversion; and (3) deriving per-sample influence scores via a forward finite difference along the previously computed parameter update direction. Experiments show that ZOIF selects data subsets with downstream utility competitive with, and in some cases exceeding, the evaluated baselines, yet at substantially lower peak
memory cost and with comparable overall computation time.
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All authors Lisong He, Yunxuan Jiang, Haishan Ye, Xiangyu Chang
Artificial Intelligence · Cross-listed from cs.LG
Anchor-and-Harvest Curriculum: A Fully Unmasked Non-LLM Curriculum for Craftax
Jiayu Zhu, Luyuan Li, Yuyuan Shi et al. (+1 more)
We introduce Anchor-and-Harvest Curriculum (AHC), a domain-informed, rule-based training system for Craftax-Full. AHC combines achievement-oriented tasks, harvested initial-state progress, difficulty adaptation, bounded selection, and original-task practice. It uses no LLM inference and keeps all 43 actions available. At 2,005,401,600 PPO rollout transitions, three primary AHC runs obtain native-task return $46.70\pm0.57$ (sample SD), compared with $41.91\pm1.01$ for fresh native PPO-GTrXL with the same student architecture and evaluation protocol. Evaluation uses ten
stochastic rollouts on each of 1024 fixed worlds per model. An additional cohort of three AHC runs reaches $46.08\pm1.23$. Two single-component ablations retain similar mean returns, while jointly freezing difficulty and removing inherited progress gives $45.14\pm2.79$. All-67-achievement profiles show that most of the return difference comes from Intermediate achievements, while the deepest-floor achievements remain unsolved. A Skill-Preflight diagnosis with a 14B teacher shows how prerequisite and floor-access constraints restrict practice. AHC and three 14B diagnostic configurations reach aggregate returns near the reported 80B DiCode mean of 45.88 and below the 235B mean of 48.55. These results show that an explicit curriculum without LLM inference can reach returns comparable to those reported for LLM-generated curricula on Craftax-Full, without action masking.
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All authors Jiayu Zhu, Luyuan Li, Yuyuan Shi, Hanyi Dong
Artificial Intelligence
SuperAI: Merging Capabilities Across Heterogeneous Foundation Models
Jingtong Dou, Chuancheng Shi, Wenhua Wu et al. (+4 more)
Enabling the reuse and composition of knowledge and capabilities distributed across foundation models is an important step toward building scalable artificial intelligence (AI) systems. However, architectural differences and incompatible representations make it difficult to transfer and integrate these capabilities through a unified cross-model interface. To address this challenge, we propose SuperAI, a cross-architecture multi-model fusion framework that integrates complementary capabilities from multiple source models into a host model through orthogonal
alignment of low-dimensional task-relevant representations. Specifically, we first extract task-relevant representations from the source models and the host model using shared anchor samples, and apply principal component analysis (PCA) to construct low-dimensional functional subspaces with the same dimensionality. Next, we use orthogonal Procrustes mappings to align the source and host coordinate systems, establishing cross-model correspondences while preserving the internal geometry of each source subspace. Finally, we train a lightweight residual multilayer perceptron (MLP) with joint supervision on task outputs and source-model increments, learning input-dependent residual increments that are injected into the host model for multi-model capability fusion. Experiments on three host models across 15 capability domains show that SuperAI achieves higher average domain scores than the evaluated cross-architecture fusion methods and better preserves general capabilities as the number of source models increases. Ablation studies further support the effectiveness of orthogonal
alignment, nonlinear residual mapping, and joint supervision.
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All authors Jingtong Dou, Chuancheng Shi, Wenhua Wu, Wei Bao, Fei Shen, Zhiyong Wang, Tat-Seng Chua
Artificial Intelligence · Cross-listed from cs.LG
Accuracy and Uncertainty in Machine Deception Detection (Research White Paper)
Heng-Sheng Chang
A deception detector is only as useful as the trust its alarms deserve. Like a polygraph, detectors that read a
language model's internal activations can catch deceptive responses that look honest. Yet current detectors depend on which layers they read, weaken on broader tests, and do not state how certain their alarms are. This white paper proposes the Machine Deception Detector, which targets both detection accuracy and the uncertainty of detector scores. The detector reads every layer of the
language model, learns which
tokens carry evidence of deception using only response labels, and calibrates its alarm threshold so that its false–positive rate stays below a prescribed level. An ensemble of such detectors measures the uncertainty of each score and raises an alarm only when its members agree, with a confidence level that sets how certain each alarm must be. The proposed evaluation trains on instructed honest and deceptive statements, tests on five unseen deceptive tasks of Liars' Bench read by six Qwen3 models from 0.6B to 32B parameters, and is designed to also show which layers carry the deception feature and how this changes with model size.
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Artificial Intelligence · Cross-listed from cs.LG
WisdomNet: trustable machine learning toward error-free classification
Truong X. Tran and Ramazan S. Aygun
Misclassification is a critical problem in many machine learning applications. Since even the classifier models with high accuracy (e.g., > 95%) still introduce some misclassification error, it may not be possible to rely on the output of a classifier. In this paper, we introduce trustable learning, which
prompts the learning model to yield only the true output, thus avoiding misclassifications. Whenever the model cannot decide the output accurately, the learning model should indicate that there could be a misclassification error if it is forced to classify, and hence, it should reject to make a decision or defer it to a human expert. Therefore, we develop a methodology for trustable learning and apply it to artificial neural networks and show that it is possible to develop a classifier with 0% misclassification error. We propose a novel neural network architecture named WisdomNet that could provide zero prediction error by introducing an additional
neuron named as conjugate
neuron that would indicate whether the network is able to classify the data correctly or not. The WisdomNet architecture can be applied to any previously built model, and we have evaluated WisdomNet with several network architectures such as multilayer perceptron, convolutional neural network, and deep network on different data sets. The results show that the WisdomNet is able to reduce the classification error rate to 0%, while labeling the data is difficult to classify as `reject' at a low percentage of within around 10%.
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Artificial Intelligence
CHEAR: Confusion-Hierarchy Evidence-Aware Reasoning for Cost-Efficient Human Activity Recognition
Xiangqing Lu, Tao Zhu, Mingxing Nie et al. (+1 more)
Large
language models (LLMs) offer a new approach to wearable-sensor-based human activity recognition.
Retrieval-augmented generation (RAG) further introduces similar motion examples and discriminative sensor knowledge, enabling decisions grounded in observations relevant to the current query. More evidence, however, does not necessarily mean better-organized evidence. When online
prompts and LLM
agents primarily orchestrate inference, decisions about which activities to compare and when to invoke discriminative knowledge remain concentrated at inference time. As the activity vocabulary grows, the model must repeatedly process more candidates and associated knowledge, whereas
pruning candidates too early risks excluding the true activity. We propose confusion-hierarchy evidence-aware
reasoning (CHEAR), which moves part of this knowledge organization offline. CHEAR constructs and validates an activity hierarchy from recurrent cross-subject
retrieval errors and builds corresponding activity-pair knowledge. At inference time, the hierarchy orders candidate comparisons; the LLM retains candidates progressively, and local discriminative knowledge is accessed only for the remaining activities. Across eight
datasets, CHEAR achieves 86.79% mean cross-subject accuracy, a 75.91% candidate reduction rate, and 96.44% true-activity retention. Per-sample
token use is 85.15% lower on average than in a variant without the hierarchical structure. Mean accuracy across eight cross-
dataset transfer directions is 88.81%. These results show that organizing candidates and discriminative knowledge around observed activity confusions reduces online LLM input while maintaining recognition performance.
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All authors Xiangqing Lu, Tao Zhu, Mingxing Nie, Liming Chen
Artificial Intelligence · Cross-listed from cs.CV
HGA-Quant: Heterogeneity-Aware Quantization for AutoRegressive Visual Generation Models
Xinmeng Liu, Xianglong Yan, Kaicheng Yang et al. (+2 more)
Recently, AutoRegressive Visual Generation (ARVG) models have emerged as a competitive paradigm for high-quality image synthesis. However, their substantial computational and
memory demands make them difficult to deploy on resource-constrained edge devices. Post-Training Quantization (PTQ) provides a promising solution to reduce the computational and
memory costs of ARVG models, yet existing methods often suffer from substantial image quality degradation under low-bit quantization. In this paper, we propose a training-free PTQ framework for ARVG, consisting of Tail-Aware Bounded-init Grid Refinement (T-BGR) and Coarse-to-Fine Activation Precision Scheduling (CAPS). T-BGR introduces a tail-aware reconstruction objective for weight quantizer calibration, assigning greater importance to distribution-tail coefficients and preserving fine-grained information without introducing additional parameters or high-precision branches. CAPS performs coarse-to-fine activation precision scheduling by jointly considering the importance and computational cost of different generation stages, enabling efficient mixed-precision allocation under a given bit budget. Extensive experiments on multiple ARVG backbones demonstrate that our framework consistently improves robustness under aggressive low-bit quantization. Notably, on Infinity, our framework achieves near full-precision image generation quality at an effective W4A5.6 activation precision, while existing methods at W4A6 still exhibit noticeable quality degradation or inconsistent performance. Code is available at https://github.com/lxm-lc/HGA-Quant.
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All authors Xinmeng Liu, Xianglong Yan, Kaicheng Yang, Weisheng Dong, Yulun Zhang
Artificial Intelligence
OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition
Xiangqing Lu, Tao Zhu, Liang Yu et al. (+6 more)
LLMs increasingly support sensor-based human activity recognition (HAR) by linking motion signals with activity semantics and physical knowledge.
Retrieval-augmented generation (RAG) grounds decisions in sensor examples and domain features, enabling training-free target-user inference with less dependence on raw numerical arrays. However, existing LLM/RAG-based HAR typically searches single-level activity spaces using static population knowledge. Consequently, they compare many unrelated candidates using the same static population evidence and overlook personalized motion patterns. We propose OntoAR-HAR, an ontology-guided agentic RAG framework combining static population and personalized knowledge. Its tree-structured activity ontology decomposes classification into local decisions among semantically related activities. The static store supplies node-constrained examples, while offline-discovered executable signal features provide numerical evidence for local distinctions. During inference, the personalized store collects selected user observations, which the LLM integrates with static knowledge and signal features while traversing the ontology to an activity label. Cross-subject and heterogeneous-sensing experiments show complementary gains from hierarchical
retrieval and personalized
memory across multiple LLM backbones, achieving state-of-the-art performance. By explicitly modeling hierarchical relations across activity granularities, OntoAR-HAR also addresses the long-overlooked semantic inclusion problem and avoids treating related labels as independent categories.
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All authors Xiangqing Lu, Tao Zhu, Liang Yu, Shuangjian Li, Lulu Ban, Furong Duan, Mingxing Nie, Liming Chen, Yaping Wan
Artificial Intelligence · Cross-listed from cs.GR
Co-DSL: Human-LLM Co-design of Reusable Spatial Concepts for Layout Generation
R. Kenny Jones, Jiajun Wu and Maneesh Agrawala
We present Co-DSL, a framework in which users and LLMs co-design reusable spatial concepts for layout generation. In the first stage, our system allows users to create a library of personalized operations. To add a concept, a user provides a description, which the LLM converts into an initial implementation. The user can then observe the concept's execution behavior over example scenarios and provide lightweight feedback to the LLM, which revises the concept's implementation logic. In the second stage, given a library, an LLM can author a layout program that meets a new layout request from a user. Executing a layout program instantiates a concept-graph, a declarative composition of library concepts. In this way, we require the LLM only to specify which concepts should hold, while we use a solver to search for a layout that satisfies them. Across five spatial layout domains, we find that Co-DSL libraries let LLMs capture layout patterns that are challenging for prior fixed-operation systems, and demonstrate that our concept-graph representation is needed for LLMs to reliably compose concepts together. We further find that Co-DSL lets LLMs author generative layout programs that expose high-level parametric controls users can manipulate to explore a distribution of related layouts without re-prompting.
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Artificial Intelligence · Cross-listed from cs.CV
Protein Crystallization Segmentation and Classification Using Subordinate Color Channel in Fluorescence Microscopy Images
Truong X. Tran, Marc L. Pusey and Ramazan S. Aygun
The accuracy of detecting
protein crystals for fluorescence microscopy images is very critical for high throughput and automated systems. Although the trace fluorescent labeling method could highlight
protein crystals, reflection and emission from the fluorescence dye is not always due to
crystal regions. Therefore, the analysis of the peak wavelength in the emission spectra of a fluorophore may not always yield effective results. In this paper, we show that using the subordinate color intensity corresponding to longer wavelengths than the peak wavelength of the emission spectra could improve the accuracy of
protein crystal detection. Hence, we have built a segmentation method based on the percentile intensity of the subordinate color for trace fluorescently labeled (TFL'd)
protein crystallization trial images. Compared to using the dominant color channel, our segmentation method on subordinate color channel was able to reduce the misclassification rate of likely-leads or
crystals as non-
crystals by the percentage of from 9.71% to 2.02% depending on the classifier. Similarly, the accuracy of classifiers were increased by the percentage of from 1.77% to 5.53%. Our method reached around 94% accuracy while keeping misclassification of likely-leads and
crystals as non-
crystals below 1%. Moreover, to evaluate the generalizability of our method, we have conducted new wet lab experiments on two
proteins, Concanavalin A (Con A) and Ab inorganic pyrophosphate (AbIPPase), and the misclassification rate was below 1%. Our experiments show that using the subordinate channel may be more helpful for TFL'd
protein trial image classification.
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Artificial Intelligence · Cross-listed from cs.CL
North or South? Diagnosing LLMs' Understanding of North Korean Language, Society, and Knowledge
Kiwoong Park, Seyoung Song, Donghyun Woo et al. (+1 more)
Large
Language Models (LLMs) have made substantial progress in multilingual question answering, yet their ability to understand low-resource sociolinguistic contexts remains under-explored. We study North Korea as a unique testbed where decades of division have produced distinct differences from South Korea in vocabulary, orthography, norms, and ideological discourse. We introduce LENS (
Language and sociocultural Evaluation for North Korean Society), a human-validated
benchmark of 1,765 questions evaluating LLMs across three dimensions: linguistic robustness, sociocultural perspective
alignment, and domain-specific knowledge. Evaluating diverse recent LLMs, we find a modest performance drop on North Korean
language inputs, a larger gap in sociocultural perspective
alignment, and severe limitations on domain-specific knowledge. Qualitative analysis reveals that models often conflate formal institutions with lived realities, project liberal-democratic assumptions, or rely on coarse stereotypes. Our results demonstrate that linguistic similarity alone does not guarantee sociocultural or domain-specific understanding, highlighting the critical need for grounded evaluation resources for underrepresented cultural and political contexts.
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All authors Kiwoong Park, Seyoung Song, Donghyun Woo, Alice Oh
Artificial Intelligence · Cross-listed from cs.CV
Rui Wang, Haoyu Zhang and Ran Cheng
Depth estimation and Gaussian reconstruction share a need for multi-scale feature processing, yet use different prediction interfaces and training objectives. Whether a decoder's internal computation can be optimized once and reused across these systems remains a structural design question. Search over primitive configurations alone leaves the organization of transformations fixed. To address this gap, we propose GeoBridge, an evolutionary framework for transferable decoder
topologies. First, GeoBridge makes stage-internal
topology searchable through residual, sequential, and parallel composition of configurable primitives. Shape-preserving composition retains the stage interfaces of a fixed multi-scale scaffold. Second, a functional-tree encoding supports stage-aware crossover and hierarchical mutation, while a gradient sensitivity-
alignment proxy evaluates candidates without training. Multi-objective
evolution balances this proxy with computation and parameter cost. Third, selected
topologies are instantiated and trained in target systems with task-specific prediction interfaces. A single source search takes approximately 12 GPU hours. GeoBridgeNet-A3 achieves the best depth accuracy among four adapted decoder baselines under a common encoder and training protocol on NYU Depth V2 and KITTI. The same
topology reaches 27.11 dB PSNR in Gaussian novel-view synthesis on RealEstate10K, and its refined representations support open-vocabulary segmentation on LERF. These results support decoder
topology as a reusable design object across the evaluated
geometric vision systems. Code and pretrained weights are available at https://github.com/EMI-Group/GeoBridge-NAS.
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Artificial Intelligence · Cross-listed from cs.LG
LeHami: Symplectic Latent Dynamics for World Models
Romina Yalovetzky, Randall Balestriero and Yann LeCun
Joint-
embedding predictive architectures (JEPAs) learn world models directly in latent space, but unconstrained autoregressive predictors may contract the latent distribution over long rollouts. We introduce Symplectic
Hamiltonian JEPA (LeHami), which replaces the standard predictor with an action-conditioned
Hamiltonian flow over a structured latent state (q, p) integrated using an exactly symplectic leapfrog step. This construction preserves phase-space volume at every rollout step and rules out uniform contraction, asymptotically stable fixed points, and merging of distinct latent states, independently of the learned parameters or action sequence. We obtain the momentum-like component from temporal derivatives of the learned position-like representation, providing an explicit second-order state for prediction and control. On conservative and actuated visual pendulum environments, LeHami substantially improves long-horizon velocity prediction over an unconstrained JEPA world model and empirically maintains constant latent volume where the baseline contracts. The learned flow also supports accurate integration at unseen sub-frame step sizes and numerical time reversal. These results show how
geometric structure can provide inference-time guarantees that are complementary to representation regularization in latent world models.
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Artificial Intelligence
RECAP: Calibrating Low-Bit Reasoning Models on the History They Read
Chi Ruan, Yujie Chen, Zixiang Zhao et al. (+2 more)
Low-bit quantization reduces the cost of serving
reasoning models but can substantially degrade long chains of thought. Each step reads cached keys and values spanning thousands of
tokens, allowing quantization errors to accumulate along the trajectory. Standard calibration operates on short segments that omit the earlier context needed by later
reasoning steps. Even when this context is retained, cache reconstruction error alone does not capture how quantization errors affect subsequent
attention. We present RECAP, a post-training quantization method that calibrates
reasoning models on the history they read. Dynamic-prefix calibration separates long historical context from short-block reconstruction. It regenerates prefix keys and values with the current quantizer and rotates the active block to supervise every position along the trajectory. Prefix read geometry further supervises historical cache errors according to their effects on
attention outputs, weighting key and value errors asymmetrically. With both components applied only during calibration, RECAP outperforms the baseline across multiple
reasoning models and quantization settings. Under W4A4KV4, RECAP improves accuracy over the baseline by 13.3 percentage points on MATH500 with DeepSeek-R1-Distill-Qwen-1.5B and 15.8 points on AIME-120 with Qwen3-4B. These results highlight the importance of preserving historical dependencies during calibration for low-bit
reasoning.
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All authors Chi Ruan, Yujie Chen, Zixiang Zhao, Jingcai Guo, Haotong Qin
Artificial Intelligence · Cross-listed from cs.CL
Sequent-Prover: Training Agents for Formal, Checkable SMT-based Reasoning
Debargha Ganguly, Weicong Chen, Mohsen Hariri et al. (+13 more)
We introduce Sequent-Prover, a family of verifier-using
language-model
agents, and the post-training framework that produces them.
Agents reason in an executable SMT-LIB scratchpad: they write complete SMT-LIB programs, query Z3 across turns, inspect solver feedback, repair formalizations, and terminate with an explicit answer. Our recipe, verifier-conditioned trajectory
distillation, bootstraps from a noisy teacher proposal policy by sampling multi-turn trajectories, retaining the successful tail that is correct, non-trivial, grounded, and executable, then applying supervised fine-tuning and trajectory-level preference optimization. Because solver success alone does not guarantee semantic faithfulness, we evaluate task accuracy, k-sample reliability, and SMT program quality. Across 37
benchmarks and 7,254 problems per model family, post-training raises the 4B model's per-attempt accuracy from 85.6% to 97.0% and four-sample all-correct reliability (pass^4}) from 64.1% to 91.7%. It also reduces average SMT calls from 4.10 to 2.52 and
tokens per attempt from 18.97k to 12.72k. Sequent-Prover
agents give answers backed by executable formal artifacts.
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All authors Debargha Ganguly, Weicong Chen, Mohsen Hariri, Vikash Singh, Sreehari Sankar, Shouren Wang, Wang Yang, Biyao Zhang, Chaoda Song, Alexander Nemecek, Kai Ye, Osama Zafar, Erman Ayday, Joe Stubbs, Srinivasan Iyengar, Vipin Chaudhary
Artificial Intelligence · Cross-listed from cs.LG
EvolvAttack: Backdooring self Evolving VLM Agents
Chuan Zou, Yang Xiang, Zexin Li et al. (+2 more)
Self-evolving mechanisms let
vision-
language model (VLM)
agents continually adapt through evolving
memory, but they also allow malicious behavior to emerge only after deployment. We introduce a new attack surface, the self-evolving backdoor, whose payload stays dormant in the initial
agent and is progressively activated as the
agent evolves. Unlike conventional backdoors, which can be exposed by testing triggered inputs on a static model, our attack requires both a visual trigger and a sufficiently evolved
memory state; static scanning with clean
memory thus yields near-zero attack success and may deem the
agent benign. We realize this through
memory-aware backdoor training, which couples trigger activation with the
agent's evolving
memory while preserving benign behavior unless the trigger co-occurs with payload-related
memory. Across three VLM backbones, four
agent pipelines, and recency-, semantic-, and reward-based
memory evolution, the attack preserves normal functionality and stays nearly inactive before
evolution (0.5% attack success rate), yet exceeds 95% attack success within 100 interactions, revealing a stealthy threat unique to self-evolving
agents.
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All authors Chuan Zou, Yang Xiang, Zexin Li, Xiaonan Luo, Simin Chen
Artificial Intelligence
ClinEdge: An Expert-Graded Benchmark of Real Patient Edge Cases Where Frontier Multimodal Models Fail
Anurag Sharma, Aditya Sharma and Sapna Baalwan
Most
benchmarks for large
language models in medicine are built from licensing examinations and theoretical questions. The few that draw on real clinical data do so only in part, and rarely require a model to read a patient's own imaging. ClinEdge addresses this gap with 32 real, de-identified patient cases from active clinical practice, each evaluated across seven frontier systems for a total of 224 expert-graded evaluations. Each case pairs primary imaging with a short vignette, contains a feature known to defeat pattern-matched
reasoning, and is scored against the patient's documented clinical course. The suite is built for depth rather than volume. Grading followed a two-stage expert process. First, a senior orthopaedic surgeon graded all 224 responses on a weighted rubric and a pass-or-fail verdict. Second, a panel of nineteen physicians, largely senior specialists at teaching medical colleges, independently reviewed every case and every one of the 224 grades. All seven production systems were evaluated through their public interfaces, and all 224 conversations are preserved as replayable links. Performance was poor across all systems, and the failures were shared: different vendors missed the same findings and converged on the same wrong plans. From the experts' notes we catalogue 18 recurring failure modes in five families. The models stop reading an image at the first finding. They give generic plans and lean toward over-treatment. They trust a misleading history over correct imaging. Their answers vary across systems and runs, and change with question format. Failing answers were delivered fluently and without any expressed uncertainty. Four cases are released in full; the remaining 28 are held out, and we invite frontier AI labs and healthcare AI companies to evaluate their models against them (research@futrbridge.com)
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Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
Framework for an AI-Ready ARPES Community Database
Zhi-Xun Shen, Jin Gene Wong, Karen Lei et al. (+41 more)
Condensed matter stands to gain profoundly from widespread adoption of artificial intelligence (AI). However, its current means of data organization impedes this endeavor. As a premier measure of electronic structure underlying material properties, Angle-Resolved Photoemission Spectroscopy (ARPES) has accumulated over four decades of spectra, yet the community's publication-centered workflow preserves only a highly selected fraction of its experimental record. A shared data ecosystem could transform these fragmented measurements and their contexts into a persistent, collective scientific
memory, creating immediate value for reproducibility and reuse while laying the foundation for AI-enabled discovery. In this Perspective, we report outcomes of a recent community workshop and propose a practical path toward such an ecosystem. Its benefits unfold along three timescales. In the near term, standardized, searchable, and citable
datasets strengthen analysis pipelines independently of AI. In the intermediate future, community-validated tools operating across laboratories could enable broader reuse and increasingly closed-loop automated experiments. In the long term, a diverse corpus of measured electronic spectra could support foundation models for
quantum matter. Looking forward, a diverse corpus of measured electronic spectra could support foundation models for
quantum matter and new discoveries. We discuss lessons from existing scientific databases, the current state of AI in ARPES, as well as areas of community convergence on data organization, access, credit, governance, and a staged pilot program.
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All authors Zhi-Xun Shen, Jin Gene Wong, Karen Lei, Leon Garcia, Felix Baumberger, Sergey Borisenko, Aaron Bostwick, Ting Cao, Tanny Chavez, Yulin Chen, Monika Choudhary, Andrea Damascelli, Dan Dessau, Thomas P. Devereaux, Sandy A. Ekahana, Alexei Fedorov, Atsushi Fujimori, Yucheng Guo, M. Zahid Hasan, Makoto Hashimoto, Yu He, Philip Hofmann, Zahid Hussain, Harold Y. Hwang, Zhurun Ji, Chunjing Jia, Timur K. Kim, Phil D. C. King, Takeshi Kondo, Alessandra Lanzara, Chun Lin, Donghui Lu, Matteo Michiardi, Sung-Kwan Mo, Brian Moritz, Laurenz Rettig, Eli Rotenberg, Takafumi Sato, Kyle M. Shen, Jonathan A. Sobota, Inna Vishik, Shuolong Yang, Ming Yi, Timothy Zaklama