Information Retrieval
Understanding ChatGPT Citations: Evidence from Production Web Search Across Model Generations
Yunxuan Jiang, Shiqi Zhang, Anqi Xing et al. (+2 more)
Generative engines such as ChatGPT Web retrieve webpages to answer user queries, but only a subset are shown as citations. Understanding this
retrieval-to-citation process is important for generative engine optimization (GEO), which aims to improve content visibility in generative-engine responses. However, prior studies mainly analyze final responses, leaving the internal execution process and its variation across model settings less understood. To this end, we conduct an observational study of four ChatGPT Web settings from ChatGPT 5.2 to 5.5, covering Instant and Thinking modes at the URL, hostname, and entity levels. We examine how ChatGPT rewrites queries, retrieves candidate webpages, and selects references. The Instant settings remain mostly single-round, while GPT-5.4 Thinking frequently performs multiple
retrieval rounds. Across repeated runs, candidates retrieved more often are also more likely to be cited. We then evaluate Google Search and the OpenAI Responses API as external proxies. Responses API candidates provide broader coverage, while its citations more closely match sources cited by ChatGPT Web. Both proxies match ChatGPT Web better at the hostname and entity levels than at the exact-URL level. Even when combined, they recover only about one quarter of cited URLs. We further test whether these external signals remain predictive when combined. Within the external candidate set, a supervised model combining them reaches AUCs above 0.74 for GPT-5.3 and GPT-5.5 at all three levels. These findings suggest that GEO should distinguish
retrieval visibility from citation visibility, account for rewritten queries and run-to-run variation, and consider hostname- and entity-level presence in addition to exact URLs. Differences across settings also suggest that GEO strategies should be re-evaluated when the deployed setting or serving environment changes.
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All authors Yunxuan Jiang, Shiqi Zhang, Anqi Xing, Xiangyu Chang, Xiaokui Xiao
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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Machine Learning · Cross-listed from eess.AS
LiveAnon: Improving Privacy and Utility in Streaming Speaker Anonymization with Codec Language Models
Yejin Kim, Kyungsu Kim, Seungu Han et al. (+1 more)
Speaker anonymization (SA) aims to conceal speaker identity while preserving linguistic content and emotion, but existing streaming systems often lose emotion information through discrete content bottlenecks or require emotion-specific modeling. We present LiveAnon, a voice-conversion-based streaming SA system that directly conditions an autoregressive codec
language model on hidden features from a pretrained streaming ASR encoder, preserving emotion without emotion-specific supervision. To reduce codec-generation latency, we introduce capped RVQ decoding, which preserves dependencies among coarse codebooks while predicting the remaining codebooks in parallel. Under VPC24, LiveAnon improves intelligibility and emotion preservation over fixed-target Stream-Voice-Anon (SVA), a strong prior streaming SA system, matches emotion-prompted SVA in emotion preservation, and achieves the strongest semi-informed
privacy among streaming systems. Under VPC26, it ranks second across all metrics among the evaluated anonymization systems, demonstrating a strong
privacy-utility balance.
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All authors Yejin Kim, Kyungsu Kim, Seungu Han, Kyogu Lee
Computational Complexity
Constant-Loss Round-Preserving Direct Sums for Information Complexity
Haoran Wang
Strong direct-sum
theorems relate the information needed to solve many independent instances of a communication problem to the information needed for one instance. For Gap-Majority composition, an optimal information bound was previously known, but preserving the number of communication rounds incurred an additional logarithmic loss. We prove a round-preserving Gap-Majority lemma with constant additive information loss, independent of both the number of instances and the number of rounds. More generally, we establish a variance direct-sum
theorem for additive square-integrable observables over product distributions. For protocols with a bounded number of terminal answer values, the extracted single-coordinate protocols preserve the round budget, and their average information cost is at most the original information cost divided by the number of coordinates, plus a term depending only on the answer alphabet and the allowed variance loss. The proof uses centered counterfactual conditional means and a global variance amortization argument. Rather than encoding absolute conditional means for every possible terminal answer, we encode the information not already determined by the common view. The total squared innovation is controlled by a telescoping variance potential, yielding an information loss independent of the number of coordinates. We also derive same-round mean-square estimation bounds for arbitrary terminal outputs.
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Robotics
Have You Seen It Clearly? Uncertainty-Guided Belief Refinement for Zero-Shot Object Navigation
Jingzhi Huang, Junkai Huang, Yuhang Peng et al. (+6 more)
Zero-Shot Object Navigation (ZSON) requires an
agent to locate target objects in unfamiliar environments without additional training for the test categories or environments. Recent approaches leverage large
language models and
vision-
language models to construct spatial representations and guide exploration. However, whether the available evidence sufficiently supports a navigation choice, and how new evidence changes the search value of previously visited locations, remain insufficiently addressed. We propose RefBeliefMap, an uncertainty-guided mapping framework that distinguishes target-location belief from spatial and semantic evidence uncertainty. By jointly modeling intra-observation uncertainty and inter-observation disagreement over a sliding
memory window, RefBeliefMap assesses the reliability of both evidence channels. When competing frontiers remain insufficiently resolved, these uncertainty estimates guide active observation and semantic
reasoning to refine the evidence supporting target-location beliefs before navigation commitment. Building on the refined map, we introduce RefBeliefNav, which jointly evaluates unexplored frontiers and historical waypoints according to their target-finding potential, expected information gain, and navigation cost. This unified objective allows historical locations to be reconsidered as new evidence changes their search value, coordinating exploration and revisitation. Experiments on HM3D and MP3D demonstrate state-of-the-art performance among the evaluated zero-shot baselines, with ablation studies examining the contributions of modules. Real-world experiments on a Unitree Go2 quadruped
robot further demonstrate the sim-to-real transferability of our method.
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All authors Jingzhi Huang, Junkai Huang, Yuhang Peng, Keyu Mu, Jiaru Zhong, Yi Wang, Lujia Wang, Lap-Pui Chau, Siyang Gao
Computation and Language
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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Computation and Language
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
Discrete Mathematics
A Four-by-Four Obstruction to Polynomial Removal for Ordered Binary Matrices
Zinan Wang
We exhibit a fixed $4\times 4$ binary
matrix with no
polynomial induced ordered removal bound. At depth $h\geq 1$, let $m=2^h$. Our explicit host has order $(14h+2)m$, exactly $2m^6+\frac{1}{2}(m-2)m^5$ induced copies, and exact editing distance $m^2$
cells. Its normalized editing distance is $\Theta(h^{-2})$, while its induced-copy density is $\Theta(4^{-h}h^{-8})$. The lower bound holds against arbitrary
cell edits through an injective family of $m^2$ protected selections. An exhaustive arithmetic classification identifies every induced copy and proves that a matching canonical repair creates none. The original-copy transversal number is exactly $m$, yielding an unbounded gap between hitting existing copies and eliminating all copies. The identities and the obstruction at every depth are formalized in Lean, with verification sources included as ancillary files.
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Machine Learning
ANTE: Anticipate, Then Execute – Learning Internal World Models for Coding Agents from Verified Beliefs
Yi Ge, Kangrui Ruan, Jifan Chen et al. (+6 more)
Rapid advances in large
language models have made software engineering
agents capable of tackling complex coding tasks. Within a containerized environment, a coding
agent receives tool responses or execution results, and determines its later actions by those earlier results. However, most existing coding
agents never look ahead: their policies do not anticipate the consequences of actions, and the terminal reward barely attributes a failure to certain earlier steps, so an ill-judged edit can corrupt the repository state irreversibly before any signal arrives. Recent code world models aim to address these challenges, but they are either trained and then frozen before reinforcement learning (RL), or reduced to an auxiliary next-
token loss on the environment's outputs. We present ANTE (Anticipate, Then Execute), an agentic RL framework that verifies the policy's pre-action predictions by real execution and turns them into per-step credit for its actions and trust-or-abstain decisions. With an internal belief stated before each action, Belief-Anchored Policy Optimization (BAPO) turns the settled predictions into per-step advantages by anchoring credit on the policy's own forecasts. To turn the trained forecasts into better actions at inference, thereby facilitating effective test-time scaling, ANTE-TTO (test-time optimization) selects among candidate actions without an external scorer. ANTE raises Qwen3-32B from 25.0% to 57.2% pass@1 on SWE-bench Verified, 6.2% above a matched GRPO baseline trained without the belief state; its world model's accuracy climbs from 44% to 69% during RL, and the learned foresight capability transfers across harnesses and out-of-distribution domains. In summary, we aim to build a code world model that knows what has happened, what holds now, and what the next action will achieve.
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All authors Yi Ge, Kangrui Ruan, Jifan Chen, Manish Prajapat, Christian Bock, Xiaoyang Wang, Rein Houthooft, Aritra Sengupta, Varun Kumar
Performance
Compositional Performance Modeling of LLM Inference Clusters with Hierarchical KV Caching
Ruiyang Ma, Sheng Qi, DaiKang Kuang et al. (+2 more)
Design-space exploration of LLM inference clusters requires predicting how hardware and serving policies interact with hierarchical key-value (KV) caching. Isolated operation profiles alone do not determine cluster latency: reuse changes computational demand, while KV location, capacity, and contention govern execution order. We present a compositional discrete-event simulator based on separating local service costs from shared resource and KV availability. Compute and transfer models calibrated on one-prefill/one-decode (1P-1D) deployments are reused across compatible workers, enabling policy and
topology comparisons without per-configuration latency fitting. Validation against SGLang covers Qwen3-8B and Qwen3-32B on 1P-1D and 3P-1D clusters. Mean request end-to-end latency error is 1.34% for question answering and 6.36% for captured coding sessions, measured as mean absolute percentage error. CPU simulation reduces replay turnaround by 15.33 times on average; real replay includes prescribed arrival gaps. Measured session-affinity and simulated retention studies demonstrate how the framework guides policy selection by predicting the joint effects on computation, latency, and KV occupancy.
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All authors Ruiyang Ma, Sheng Qi, DaiKang Kuang, Teng Ma, Guojie Luo
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
Robotics
LERR-Nav: Learning to Coordinate Exploration, Recovery, and Reminiscing for Object Navigation
Jingzhi Huang, Junkai Huang, Yuhang Peng et al. (+6 more)
Object Navigation in unseen environments remains a fundamental challenge in embodied AI, as it requires
agents to generalize visual and spatial
reasoning to novel layouts. While recent advances in large
language model (LLM) and
vision-
language model (VLM) have enhanced semantic mapping, spatial
memory, and waypoint selecting for navigation, successful navigation requires more than selecting the next promising waypoint. A
robot must also recognize stalled progress, escape unproductive behaviors, and revisit overlooked target cues. Addressing these challenges requires both specialized navigation strategies and a high-level policy that coordinates their execution. Motivated by the analysis of failed navigation episodes, we propose LERR-Nav, an object navigation framework that coordinates exploration, recovery, and reminiscing through a learned high-level policy. The framework comprises four complementary navigation strategies: value-function-guided exploration, LLM-guided exploration, recovery from stalled or repetitive behavior, and
memory-guided reminiscing. To support history-aware coordination, we maintain the execution
memory that preserves textual summaries of all strategy executions alongside a sliding window of map sequences. To learn the coordination policy, we initialize a high-level
vision-
language agent from Qwen2.5-VL-7B-Instruct and train it through a two-stage curriculum. The first stage uses map-centric supervised fine-tuning (SFT) to establish spatial grounding. The second stage performs SFT on variable-length navigation segments to learn which navigation strategy to invoke next based on current maps and the hybrid execution
memory. Experiments on HM3D and MP3D demonstrate state-of-the-art navigation performance, while ablation studies validate the contributions of individual components.
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All authors Jingzhi Huang, Junkai Huang, Yuhang Peng, Keyu Mu, Hailong Huang, Haoyang Yang, Renbo Luo, Lujia Wang, Yi Wang
Machine Learning
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
Computer Vision
UniForge3D: Joint 3D Reconstruction and Generation in a Shared Anchor-Guided Space
Wenjue Chen, Wenyue Chen, Heliang Zheng et al. (+3 more)
Generating complete 3D assets from sparse, unposed images requires preserving observed geometry while completing unseen regions. Joint reconstruction and generation offers a way to address both goals: reconstruction recovers the geometry observed in the input images, while generation contributes learned shape priors. However, combining camera-relative reconstruction with canonical-space generation introduces a coordinate mismatch that makes direct
geometric interaction more difficult. Moreover, conditioning each 3D location on all views without accounting for their relevance may mix useful
geometric evidence with unrelated image content, hindering local detail recovery. We present UniForge3D, which jointly models reconstruction and generation in a shared anchor-guided space, resolving their coordinate mismatch. This space follows the anchor view's azimuth while keeping the object upright. The two branches exchange features bidirectionally to predict per-view point maps and a complete sparse voxel structure. We further condition point-map prediction on the generated structure to establish pixel-to-voxel correspondences and select the most informative input view for each voxel. Features from the selected views then guide detailed shape generation, helping preserve observed
geometric details. Our method achieves state-of-the-art appearance and geometry results on Toys4K and GSO, generating complete 3D assets with improved fidelity. Project webpage: https://xishuxishu.github.io/uniforge3d-project/
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All authors Wenjue Chen, Wenyue Chen, Heliang Zheng, Rongfei Jia, Yuan Liu, Meng Li
Sound
JSIR: An Audio-Aligned, Multi-Task Dataset of Jazz Standard Recordings
Sihun Lee and Dasaem Jeong
Beat tracking, chord estimation, and structure analysis are staple tasks of music information
retrieval. Their
datasets are drawn largely from Western popular music, and jazz remains a repertoire on which state-of-the-art systems underperform. We present JSIR, the Jazz Standards
dataset for Information
Retrieval. Its 267 recordings of 44 jazz standards span 25.6 hours, 3.7 times the audio of JAAH, the closest existing resource. For each recording, we provide annotations for beat and downbeat positions, chords, and sections, all produced by a jazz musician and revised in several passes. In baseline experiments, models trained with JSIR also improve on other jazz
datasets: the downbeat F-measure of a state-of-the-art beat tracker rises from 87.7% to 89.2%, and the seventh-chord accuracy of a pop-trained chord
estimator on JAAH from 34% to 41%. We publicly release the annotations, the annotation tool used to create them, and a web application that displays them in sync with the audio.
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Computer Vision
Video-Based Sexual Harassment Detection Using Fine-Tuned Video Transformers
Jubayer- Al- Mahmud, Md Shamimul Islam and Md Nur Hossain
Sexual harassment represents a pervasive societal challenge, with a substantial proportion of incidents remaining unreported due to insufficient evidence and delayed intervention mechanisms. Smart surveillance systems capable of detecting such incidents in real-time could assist law enforcement and serve as a deterrent. However,
video-based harassment detection poses significant challenges, including variations in clothing, illumina- tion, partial occlusions, low resolution, and diverse camera view- points. Despite its social importance, this problem has received limited research
attention, primarily due to the scarcity of large- scale annotated
video datasets. To address this gap, we extend the Sexual Harassment
Video (SHV)
dataset to 600 short
video clips sourced from YouTube and annotated into harassment and non-harassment categories using consistent labeling guidelines. We fine-tune three state-of-the-art
video transformer models, VideoMAE, ViViT, and TimeSformer, pretrained on Kinetics- 400, for binary harassment classification. Experimental results show that all three models achieve competitive classification performance, with TimeSformer attaining the highest test accu- racy of 92.27%, outperforming VideoMAE 91.71% and ViViT 91.16% on the extended SHV test set. Our analysis reveals that performance is notably influenced by
dataset size,
video quality, frame sampling strategy, and model architecture. This work presents a comprehensive evaluation of modern
video transformers for this sensitive real-world task.
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Machine Learning
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
Sound
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
Machine Learning
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
Computer Vision
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
Multimedia
SIMPLE: Semantic Structure-Aligned Prompt Learning for Open-Vocabulary Audio-Visual Event Localization
Hanyu Xuan, Haoran Xu, Min Yin et al. (+7 more)
Open-vocabulary audio-visual event localization aims to infer explicit segment-level categories for both seen and unseen audio-visual events. Existing solutions rely heavily on hand-crafted
prompt templates, whose predictions are highly sensitive to subtle wording variations. Learnable
prompts remove this manual tuning burden, but when trained solely on seen events, they tend to overfit to seen categories and distort the inter-event semantic
topology required for generalization to unseen categories, a failure we term semantic
topology drift. In this paper, we propose SIMPLE, a semantic structure-aligned
prompt learning framework that aligns cross-category semantic
topology between learnable and hand-crafted
prompts across asynchronous audio and visual streams. Specifically, our SIMPLE combines Learnable Continuous
Prompt Tuning (LCPT) with Semantic
Topology Consistency Regularization (STCR). The LCPT automates
prompt engineering through data-driven optimization, while the STCR module aligns the category-wise similarity distributions of learnable and hand-crafted
prompts under random category subset sampling to alleviate semantic
topology drift. Comprehensive experiments and analyses on the OV-AVEBench demonstrate that our SIMPLE framework achieves state-of-the-art performance on the seen and unseen splits, while using roughly half the trainable parameters and roughly one-third the training time.
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All authors Hanyu Xuan, Haoran Xu, Min Yin, Shuo Wang, Cheng Zhao, Kun Li, Fei Wang, Zhiliang Wu, Guanghui Yue, Hehe Fan
Computer Vision
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
Robotics
CoLA: Multi-VLA Coordination via Learned Adapters
Ahaskar Kashyap, Jiawen Zong, Boyu Ma et al. (+4 more)
Vision-
Language-Action (VLA) models are typically deployed as isolated single-
agent policies. When several such
agents share a workspace, they can coordinate implicitly, by observing one another, or explicitly, by exchanging messages, but it remains unclear when explicit communication becomes necessary. We introduce CoLA (Coordination via Learned Adapters), which keeps each
agent's VLA backbone frozen and trains a lightweight message channel and a per-arm
diffusion action head on top, with fully decentralised execution. Across three tasks that differ in how much each
agent can observe, the channel matters only when information is private. On an occluded tray handover, where the box's colour marker is visible to one
agent only, CoLA reaches 76.0% success versus 1.3% with messages zeroed throughout training. On two-arm and three-arm handover tasks, removing messages has no measurable effect with a shared overhead camera (90.6% vs. 88.3% at two arms; 63.5% vs. 60.0% at three), but lowers success on the two-arm task's wrist-only variant (74.8% vs. 64.3%). This architecture generalises across different VLA backbones as well. With a frozen $\pi_{0.5}$ backbone on the same occluded tray task, CoLA matches the overall success rate of a LoRA-fine-tuned baseline (37.3% vs. 34.7%) using a fraction of the trainable parameters. When CoLA places the box, it selects the correct tray 71.8% of the time, whereas the independently fine-tuned baseline, whose placing arm never sees the marker, selects it at chance level (34.7% against 33.3%).
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All authors Ahaskar Kashyap, Jiawen Zong, Boyu Ma, Chuhao Zhou, Jingliang Li, Stefano V. Albrecht, Jianfei Yang
Machine Learning
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
Robotics · Cross-listed from physics.bio-ph
Sensing via whole-body interactions with complex terrains: physical insights from robotic models
Chen Li
In nature, animals interact with complex terrains and often generate forces, deformations, and vibrations across the body and environment. Such interaction-induced mechanical information may guide behavior, yet its generation and use are difficult to resolve because movement rapidly and continually reconfigures the interactions that produce it. Sensory neurophysiology and biomechanics have revealed how mechanical information is transformed within sensory, neural, and musculoskeletal subsystems, whereas studies in complex terrains reveal rich whole-body interactions and behavioral outcomes. Connecting these levels remains a challenge. Here, I present a focused perspective on how biologically guided robotic physical models can help identify candidate mechanical information generated through whole-body interactions, test its physical mechanisms and behavioral benefits, and generate biological hypotheses. Three case studies illustrate this approach, multi-legged locomotor transitions to traverse dense clutter, limbless vertical body bending to propel through uneven terrain, and rapid spider leg crouching to help vibration sensing of prey on a web. Across these systems, correspondence between
robots and animals supports the biological plausibility of these candidate physical mechanisms and motivates better measurements and models to determine how the relevant mechanical information is sensed and used to modulate behavior.
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Machine Learning
Full Waveform Inversion with a Learned Preconditioner
Jeongjin Park
Full-waveform inversion (FWI) is a powerful approach for reconstructing subsurface velocity models, but its practical performance is limited by ill-conditioning, sensitivity to the update direction, and the high cost of
curvature-based methods. In this project, we investigate whether a neural operator trained on reverse-time-migration (RTM) images can provide a useful learned preconditioner for FWI. We compare projected FWI against neural-preconditioned FWI using either a standard
Fourier neural operator (FNO) or a Jacobian-informed FNO (JI-FNO). The neural preconditioner applies the normal operator of the learned RTM map to modify the true Devito FWI gradient. Our experiments show that the JI-FNO preconditioner achieves the lowest model error while maintaining data-residual decay comparable to baseline projected FWI. In contrast, the standard FNO preconditioner is unstable and gives worse model recovery, despite using the same neural-preconditioning framework. We also identify a failure mode of a nonlinear neural Gauss Newton-type inner solve: directly matching the neural RTM output to the FWI gradient creates an objective-space mismatch and does not yield a reliable descent direction. These results suggest that forward RTM-image prediction alone is insufficient for learned preconditioning; accurate learned derivatives and objective-consistent use of the neural operator are critical for producing useful inversion updates.
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Computer Vision
BaT-SAM: Introducing Inductive Bias into SAM via a Mixture of Convolutional Experts for Brain Tumor Segmentation
Sumiaya Jannat, Md Mahedi Hasan, Md Shamimul Islam et al. (+1 more)
Accurate
brain tumor segmentation from
magnetic resonance imaging (MRI) is essential for diagnosis and treatment planning; however, manual delineation is labor-intensive and time-consuming. Existing approaches are either task-specific with limited generalization or rely on user-provided
prompts during inference. We present BaT-SAM, a parameter-efficient adaptation framework that enables fully automatic
brain tumor segmentation with the Segment Anything Model (SAM). BaT-SAM injects local inductive bias into SAM's frozen
Vision Transformer (ViT) encoder via Conv-LoRA, where image features are processed by a sparsely gated mixture of multi-scale convolutional experts. This design compensates for the limited translation-equivariant inductive bias of vanilla ViTs while fine-tuning fewer than 5M parameters (less than 1% of the full model). To remove the need for user
prompts, we further introduce a lightweight self-prompting box regressor that is jointly optimized with the adapter using scheduled sampling and predicts tumor locations directly from image
embeddings. On the BRISC2025
benchmark, BaT-SAM achieves a weighted mean IoU of 0.832 while outperforming state-of-the-art
benchmarks. It further demonstrates strong cross-
dataset generalization, achieving Dice scores of 0.702 on Figshare and 0.784 on the LGG MRI Segmentation
dataset.
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All authors Sumiaya Jannat, Md Mahedi Hasan, Md Shamimul Islam, Md Nur Hossain
Information Theory
New Minimum Storage Regenerating Codes With Small Sub-packetization Levels for All Admissible Repair Degrees
Jie Li, Xiaohu Tang and Linqi Song
In this paper, {we propose a new family of $(n, k)$ Minimum Storage Regenerating (MSR) codes} for distributed storage systems. By structuring the parity-check
matrix as a block Vandermonde
matrix composed of diagonal
matrices and generalized permutation
matrices, the proposed code achieves a sub-packetization level of $N = (d-k+1)^{\lceil\frac{n}{2}\rceil}$ and supports any repair degree $k+1\le d<n$. A key innovation of the new construction lies in relaxing the strict commutativity condition previously required for the building blocks in the parity-check
matrices of Ye-Barg codes. We introduce a novel proof technique to establish the MDS property without relying on full commutativity, thereby generalizing the applicability of block Vandermonde
matrices and enriching the family of Ye-Barg codes. Additionally, the sparsity of the parity-check
matrix enhances encoding and decoding efficiency by reducing computational complexity. This work provides new insights into the construction of MSR codes.
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