Game Theory
LangBP: Language-Guided Reasoning and Acting for Joint Bidding and Pricing
Jiaqi Ding, Chuan Yang, Linghui Meng et al. (+5 more)
Auto-bidding is a long-horizon sequential decision problem for maximizing conversion value under budget and key performance indicator (KPI) constraints. Recent work extends this task from bidding alone to joint bidding and pricing, where a policy controls bidding decisions and pricing corrections. Existing methods mainly rely on numerical trajectory modeling, which offers limited support for interpreting campaign context and expressing high-level strategies. Large
language models (LLMs) can complement this paradigm with their
reasoning capabilities. However, existing
language-guided methods have two limitations. First, they condition actions on
language strategies without modeling the corresponding state changes, making it difficult to distinguish errors in strategy understanding from errors in action generation. Second, different instructions can produce similar execution effects, leading to imbalanced policy updates across effects. We propose LangBP, a hierarchical framework for
language-guided joint bidding and pricing. LangBP's Semantic Decision
Transformer (S-DT) predicts target states from the instruction and the trajectory history, then recovers the joint action via inverse dynamics. We further propose Execution-Grouped Policy Optimization (EGPO), which scores candidate effects with a Context–Effect Verifier (CEV) and balances policy updates across effect groups. Experiments on AuctionNet show that LangBP outperforms strong baselines, and online A/B tests further demonstrate business gains in real-world deployment on a large-scale e-commerce platform.
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All authors Jiaqi Ding, Chuan Yang, Linghui Meng, Shengsheng Niu, Jie He, Zhangang Lin, Ching Law, Xiaolin Fang
Computer Vision
CapFrame: Text-Instructed Viewpoint Grounding in 3D Gaussian Scenes via Geometric Pseudo Labels
Jirong Li, Satoshi Ikehata, Shuhei Kurita et al. (+1 more)
3D Gaussian Splatting (3DGS) enables photorealistic real-time novel view synthesis, yet placing a virtual camera to capture a desired frame remains largely manual. Existing
language-guided approaches in 3D scenes mainly focus on object-centric grounding, determining what to observe but rarely controlling how it should appear in a single frame, such as subject orientation or frame layout. To address this limitation, we introduce a new task, Text-Instructed Viewpoint Grounding (TIVG), which aims to identify a 6-DoF camera pose in a 3D Gaussian scene whose rendered frame aligns with a text instruction. To solve this task, we propose CapFrame, a partially differentiable framework that converts
language into
geometric pseudo labels for camera pose optimization. CapFrame follows a Retrieve-Translate-Refine pipeline: it retrieves relevant views and ranks them through a Question-Evaluation process with MLLMs, translates the instruction into orientation and layout pseudo labels, and refines the camera pose via differentiable optimization with layout and orientation losses in 3DGS. Experiments on 38 real-world scenes with 135 instructions indicate that CapFrame produces viewpoints better aligned with texts than heuristic viewpoint search and adapted trajectory generation baselines, validated by VLM metrics, MLLM judges, and user studies. Code is available at: https://github.com/jirongli/CapFrame
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All authors Jirong Li, Satoshi Ikehata, Shuhei Kurita, Ikuro Sato
Human-Computer Interaction
DOBI: Dynamic Opportunistic Body Input via Spare Joint Recruitment for Hands-Free XR
Rachel Kim, Xun Qian and Sang Ho Yoon
Extended Reality (XR) systems are often most useful when users are engaged in ongoing physical tasks, yet current interaction techniques still largely assume the hands are available. We present opportunistic body input, an interaction paradigm that redirects continuous XR control to whichever available body region remains free in the moment. To investigate how users naturally coordinate these spare-body movements, we conducted an elicitation study across six hand-busy scenarios. We found that while users' preferred spare body regions shift dynamically based on physical constraints, the resulting spontaneous movements share a consistent, low-dimensional kinematic structure organized around a dominant principal axis. Building on these findings, we present DOBI (Dynamic Opportunistic Body Input), a real-time XR technique that uses gaze to target a UI element, a brief trigger gesture to identify the recruited spare body region, and the region's subsequent motion to drive continuous 1D control. A 1D Fitts' law study establishes the baseline motor performance of this paradigm across four distinct body regions, achieving throughputs up to 2.62 bits/s with an overall 5.0% error rate, and a dual-task usability study shows that DOBI supports reliable, low-effort control (SUS = 84.2) while users remain engaged in realistic hand-busy activities.
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Computational Complexity · Cross-listed from quant-ph
Exact quantum splitting and the structure of finite algebras
Muhammad Imran
For squarefree $f\in\F_q[x]$, Berlekamp's fixed-point
algebra is $B\cong\F_q^r$, where $r$ is the number of irreducible factors. We give an exact
quantum splitting procedure in a
circuit model permitting efficiently computable, input-dependent single-
qubit rotations. For a block with $s\ge2$ factors, a quadratic-character test (odd $q$) or absolute-trace test (characteristic $2$) is nonconstant with an exactly known probability $p_{q,s}\ge1/2$. Its value depends only on $q,s$, not on the hidden factors. An auxiliary-
qubit reduction to success probability $1/4$ makes one Grover iteration exact. Thus factorization takes exactly $r-1$ splitting rounds and $O(n^3\log q)$
quantum $\F_q$-operations, with deterministic
polynomial classical preprocessing and bookkeeping. The method also extracts primitive idempotents of arbitrary finite-dimensional commutative unital
algebras given by structure constants in $O(n^4\log q)$
quantum $\F_q$-operations, staying over $\F_q$ throughout. Computing the radical and passing to the center extends it to the decomposition of $A/\Rad(A)$ into simple two-sided ideals, for an arbitrary finite-dimensional associative $\F_q$-
algebra $A$.
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Machine Learning
Coarse composition suffices: tabular in-context learning for multi-activity antimicrobial peptide profiling
Anuj Pal, Raunak Kumar, Dhruvi Solanki et al. (+3 more)
Antimicrobial peptides (AMPs) often act against multiple pathogen classes, making multi-label activity prediction a more realistic screening target than binary antimicrobial classification. The ESCAPE
benchmark formalizes this setting, but leading approaches typically rely on
multimodal, structure-conditioned deep models that are costly to train and tune. We show that a simple, sequence-only pipeline can match and surpass these methods by combining 330 interpretable sequence descriptors with TabPFN, a tabular foundation model that performs in-context prediction in a single forward pass without gradient-based training or hyperparameter search. On ESCAPE (82,359 peptides; five labels), a label-powerset TabPFN model achieves mAP-5 = 77.8%, improving on the previously best reported 72.1%. A probabilistic classifier chain is the first method to match or exceed the best published average precision on each of the five labels simultaneously. The gains persist under the prior state-of-the-art single-fold training protocol, indicating they are not a training-set-size artefact, and are largest for remote homologues (+11.2 points below 30% sequence identity). Ablations further show that predicted structure is unnecessary at inference and that performance is not driven by any single descriptor family: ten global physicochemical scalars recover 91% of full-feature performance. Finally, explicitly modelling label dependence yields targeted benefits for scarce activities and supports ranking which activity to assay next from partial positive evidence.
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All authors Anuj Pal, Raunak Kumar, Dhruvi Solanki, Parikshit Pareek, Juhi Singh, Jitin Singla
Information Retrieval
Beyond Ranking Accuracy: Evaluating LLM-Cited Feature Rationales for Next Basket Repurchase Recommendation
Yanan Cao, Anay Dombe, Murali Mohana Krishna Dandu et al. (+5 more)
Next-basket repurchase recommendation is commonly formulated as a ranking task: given a customer's purchase history, the system ranks previously purchased items that may be needed again. In production settings, however, ranking accuracy is only one component of recommendation quality. Customers may also benefit from concise evidence about why an item is recommended now. Large
language models (LLMs) offer a potential way to surface such evidence through feature-based, human-readable rationales grounded in interpretable behavioral signals. We construct repurchase features spanning cadence, frequency, recency, user behavior, and item popularity, and evaluate LLMs on two public grocery
datasets and one proprietary retail
dataset. We investigate (1) whether off-the-shelf LLMs can use these features as next-basket scorers relative to heuristic and supervised rankers, and (2) whether LLM-cited features carry outcome-grounded ranking signal. For the latter, we compare LLM-cited features with model-specific attribution methods under a cross-model feature-masking protocol that measures ranking degradation after masking selected features. Our results show that LLM scores are not competitive with supervised rankers, suggesting that off-the-shelf LLMs should not be used as standalone repurchase recommenders. However, changes in
prompt and evidence representation can improve outcome-grounded feature-masking results in some settings even when ranking performance does not improve; the effect is
dataset-dependent and does not consistently match attribution baselines. These findings suggest a practical role for LLMs as validated explanation components rather than primary rankers, with rationale quality evaluated separately from ranking accuracy.
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All authors Yanan Cao, Anay Dombe, Murali Mohana Krishna Dandu, Shreeranjani Srirangamsridharan, Sinduja Subramaniam, Yogananth Mahalingam, Evren Korpeoglu, Kannan Achan
Cryptography and Security
A Roadmap to Available ICS Datasets and Testbeds for Cybersecurity Research
Ebtesam J. Alqahtani and Mohammad Hammoudeh
Industrial Control Systems (ICS) are the backbone of many critical infrastructure sectors; however, their growing level of connectivity, long lifespan and integration with the Information Technology (IT) environment introduces numerous cybersecurity challenges. The merging of Operational Technology (OT) and IT along with the deployment of Industry 4.0 technologies increases the attack surface of ICS environments, which in turn makes them more vulnerable to advanced cyber threats. Therefore, many researchers have shown interest in the field of cybersecurity of ICS. The topics of intrusion detection, anomaly detection, threat intelligence, attack simulation and resilience assessment of ICS have received much
attention. Nevertheless, the development and testing of cybersecurity solutions for ICS remains to be challenging due to the lack of appropriate
datasets and experimental environment. The main objective of this paper is to provide the roadmap of existing ICS cybersecurity
datasets, testbeds and digital twins. This paper presents various taxonomies along with systematic analysis of architecture, characteristics, capabilities, pros and cons of these tools. The results of the analysis demonstrate the presence of persistent problems such as lack of standardized benchmarking
datasets, lack of modern attack scenarios, insufficient number of
datasets based on real operational traffic and difficulty in validating artificial intelligence-driven cybersecurity solutions. In addition to summarizing current research on ICS cybersecurity
datasets and testbeds, this roadmap provides the identification of research gaps and recommendations on creation of new tools.
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Sound
Ouroboros: Self-Referential Backdoor Attacks on Speech Enhancement via Clean Audio Triggers
Yunjie Zhou, Yuheng Huang and Diqun Yan
Speech enhancement models are widely deployed as frontend modules in real-time
speech services, yet their vulnerability to
backdoor attacks remains unexplored. Existing
backdoor methods are confined to classification tasks and rely on active trigger injection, an assumption incompatible with the passive processing nature of
speech enhancement models. In this paper, we propose Ouroboros, a novel
backdoor attack framework that leverages the ideal clean outputs of
speech enhancement models as natural triggers, enabling inference-time activation without any external trigger injection. Extensive evaluations show Ouroboros achieves near-perfect attack success rates with minimal performance degradation on diverse models and
datasets. Physical-world validations confirm that naturally recorded, unaltered clean audio can reliably activate the
backdoor. Moreover, Ouroboros generalizes to targeted content-tampering attacks and remains effective against common filtering and finetuning defenses.
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Machine Learning
Learning PDE Time-Stepping with Neural Cellular Automata
Esha Saha and Hao Wang
Classical numerical solvers for partial differential equations (PDEs) are computationally expensive to solve repeatedly across varying initial conditions, motivating the need for learned surrogates. In this paper, we propose a trainable Neural Cellular Automata (NCA) based surrogate model for learning long time PDE dynamics. Rather than mapping an entire initial field to a full trajectory in one shot, our proposed model learns a small, local, homogeneous update rule that is applied identically and repeatedly at every grid
cell, mirroring the locality of differential operators. We
benchmark this framework against three baselines: PDE - Net, a modified physics-informed neural network (PINN), and a
Fourier Neural Operator (FNO), on five canonical PDEs (heat, advection, Burgers, Allen - Cahn, and Fisher - KPP), evaluated at temporal domain two times beyond the training temporal domain. The proposed model achieves the lowest long-horizon relative errors on the majority of the experiments.
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Computation and Language
Do Small Models Use the Law You Give Them? Measuring Context Use on a Bilingual Bangladesh Legal Benchmark
Moniruzzaman Mahadi, Abrar Mohammed Tanzim Alam, Sayma Siddika Monalisa et al. (+3 more)
Fine-tuning can improve legal question-answering accuracy without improving how models use law supplied in context. We study this distinction in bilingual Bangladeshi legal QA, where observed errors can arise from answer scoring,
retrieval, or failure to use relevant law. We construct a hierarchy-preserving statutory corpus, 2,165 reviewed bilingual fine-tuning examples, and a 150-item supplied-law control. We evaluate six instruction-tuned models: Llama-3.2-1B, Llama-3.2-3B, Qwen3.5-0.8B, Qwen3.5-2B, Qwen3.5-4B, and Gemma-4-E2B, with three LoRA seeds per model. To separate effects, we combine constrained option-letter scoring, cyclic option rotation, and controlled removal of the governing provision. On 398 Bar Council outputs, an exact-line parser attributes an accuracy gain of 50.0% to the Qwen3.5-2B seed-42 adapter, whereas option scoring yields only $3.0\%$. For Gemma-4-E2B, the two scoring methods favor different systems. When the governing provision is guaranteed to be present, five of six reference models improve by $14.7\%-19.3\%$ under the four-order criterion. Removing that provision reduces accuracy by $8.0\%-15.3\%$ for models and by $13.8\%-14.9\%$ points for their adapters. However, difference-in differences estimates show no increase in reliance on the governing provision after fine-tuning. Results show that legal adaptation claims require separating scorer, retriever, and model effects. Our Code and data are available at https://anonymous.4open.science/r/bangladesh-legal-qa-11E3
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All authors Moniruzzaman Mahadi, Abrar Mohammed Tanzim Alam, Sayma Siddika Monalisa, Mir Mohammad Asif Abdullah, Swakkhar Shatabda, Md Adnan Arefeen
Sound
Parallel Time-Band Mixing with Learned Observation-Adding for Robust ASR Front-Ends
Xingyu Shen, Runze Wang, Wei-Ping Zhu et al. (+1 more)
Speech enhancement is often used as a front-end for robust ASR, yet recurrent temporal and cross-band modules introduce sequential dependencies that reduce parallel efficiency. In this paper, we present a sequence-parallel band-split enhancement front-end built on a Parallel Time-Band Mixer (PTBM) block that eliminates within-block recurrent unrolling. PTBM integrates intra-band temporal mixing and per-frame cross-band
attention within a unified parallel architecture, enabling efficient contextual modeling across both time and frequency dimensions. The system retains the mask-plus-residual reconstruction interface and introduces learned Observation-Adding (LOA) to suppress ASR-sensitive artifacts without development-set tuning. Experiments on DNS Challenge and CHiME-4 with frozen Whisper back-ends show that the proposed front-end consistently reduces word error rate relative to recurrent band-split baselines while requiring only 0.96 M parameters and 0.58 GMAC/s for the front-end network.
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All authors Xingyu Shen, Runze Wang, Wei-Ping Zhu, Benoit Champagne
Computation and Language
Sequential Trajectories and Simultaneous Blending: Multi-Emotion Modeling for Instruction-Following TTS
Yan Zhou, Yun Hong and Yang Feng
Natural-
language instructions enable flexible control of synthesized
speech, yet emotional TTS systems primarily model a single utterance-level affect, leaving multi-
emotion control underexplored. We study two complementary multi-
emotion TTS tasks:
emotion trajectory, which spans several ordered affective stages, and
emotion blending, in which multiple
emotions coexist throughout an utterance. These tasks expose a supervision mismatch: supervised fine-tuning (SFT) does not explicitly evaluate
emotion features, while single-
emotion rewards provide neither structure-aware feedback for trajectory completion nor pair-aware feedback for blending. We introduce HybridEmo, a post-training framework that initializes both tasks with SFT and then aligns the
speech-
token policy through Group Relative Policy Optimization using a sample-aware hybrid reward. For trajectory samples, segment-aligned consistency combines average and weakest-stage evidence to preserve the correctness and completeness of prescribed stages. For blending samples, a GMM-based reward combines frame-level support from the union of target-
emotion anchors in an offline
emotion space with an utterance-level weaker-target margin. Both branches share an ASR reward and are routed within a unified policy. On MultiEmo-Test, HybridEmo significantly improves trajectory correctness and blending intensity, without a noticeable degradation in speaker similarity. Human evaluation prefers HybridEmo to CosyVoice 3 and EmoVoice-0.5B, with nearly balanced preferences against Qwen3-TTS.
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Machine Learning
Generative multi-domain transfer learning for fault detection in data-scarce wind turbines
Stefan Jonas and Angela Meyer
Normal behavior models have shown promise for reliable fault detection in wind turbines. However, these unsupervised anomaly detection models require sufficient fault-free training data to learn the normal operation behavior of turbines. Under data scarcity, for example in newly deployed wind turbines, these models may result in poor fault detection performance. In this work, we propose a multi-domain generative domain mapping approach based on
Star Generative
Adversarial Networks (StarGAN) to improve fault detection on data-scarce wind turbines. Our model maps SCADA measurements from a data-scarce turbine to resemble those of several data-rich turbines. By preserving the operational state during translation, faults occurring in a data-scarce domain can be mapped and detected by reliable pre-trained normal behavior models of data-rich domains. Highlighting the benefits of an ensemble fusion strategy, we show that under severe data scarcity our method can produce anomaly scores comparable to models trained on large representative
datasets. Our approach can consistently outperform models trained on scarce data when less than 2 weeks of training data are available. With just 2 weeks of accumulated training data, we achieve an anomaly score similarity that is, on average, +16% higher than conventional fine-tuning, and +10% higher than single-source domain mapping. As a step towards unsupervised model selection, we propose a proxy metric that detects poor performance at training time, despite an absence of anomalies. Our study presents the potential and challenges of multi-domain mapping for wind turbine fault detection under unrepresentative training data.
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Artificial Intelligence
Ignorance or Incompetence? Constructing Knowledge-Gated, Verifiable Tasks for LLM Agents
Hanlin Tian, Minhao Li, Yu Mi et al. (+5 more)
Professional
agent tasks often depend on conventions that are absent from public corpora, yet
benchmarks rarely control whether an
agent has access to those conventions. We introduce a knowledge-gated task-construction protocol that separates a task instruction from a compact artefact containing private conventions, reference tables, and utility operators. Construction-time provenance, byte-identical task instructions across the provided- and withheld-artefact conditions, leak audits, and executable witnesses make dependence on the artefact explicit and testable. Across fifteen calibration tasks, one frontier
agent configuration achieves a 68.0% pass rate with the artefact and 0% without it; on one task, a plausible but incorrect artefact also yields 0% across five trials. Deterministic solvers and rule corpora provide exact ground truth for structured tasks, while named criterion-level rubrics support outputs that cannot be checked by a single executable oracle. A configuration-relative calibration screen retains seven tasks satisfying our five-trial empirical knowledge-gating screen. These experiments validate the behavior of the construction protocol; they do not establish that the retained tasks improve post-training. We publicly release part of the task suite and supporting tooling at https://github.com/DatagridsAI/Knowledge-Gated-Task-Construction.
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All authors Hanlin Tian, Minhao Li, Yu Mi, Sihan Zhu, Zhao Yang, Yuxiang Wang, Hongquan Zhu, Qiufei Hu
Computation and Language
On the Design of Qwen3.8-Next Architecture: Evaluation, Efficiency, and Training Stability
Zihan Qiu, Zekun Wang, Xiao Li et al. (+33 more)
We describe the architecture and ablations of Qwen3.8-Flash-Next, a sparse mixture-of-experts model with 125B parameters, 6B activated per
token, and additional 51B parameters of n-gram
embedding tables held off the accelerator. On fourteen pre-training
benchmarks the model leads the 397B-A17B predecessor on eight and trails it on the rest by at most 2.6 points, at 1/3 the activated parameters, 1/3 the training
tokens, and roughly 1/9 the training FLOPs.
Token mixing uses a layer-wise hybrid of Gated DeltaNet (GDN) and global
attention, with one full-
attention layer in every four; at continued-pretraining time those full-
attention layers are replaced by Qwen Sparse
Attention (QSA), which scores context at micro-block granularity with a compressed lightweight indexer. The residual stream is widened to four branches and read through an elementwise gate, a design we call the Gated Residual (GR). Capacity is added outside the backbone by a single n-gram
embedding layer whose tables are prefetched from host
memory. We evaluate every candidate change along three axes: loss together with downstream
benchmarks; the cost of the change in training, prefill and decode; and its effect on the optimal hyperparameters and training stability. Loss and downstream accuracy do not always move together: enlarging the n-gram vocabulary lowers loss monotonically while downstream accuracy saturates. The architecture and the
Muon optimizer together shift the optimal learning rate and batch size upwards, render batch-size warmup unnecessary, and substantially improve stability under stress tests. Loss,
benchmarks, efficiency and stability form one design problem. Solved jointly, they yield a recipe that is simultaneously more efficient, more capable and more stable.
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All authors Zihan Qiu, Zekun Wang, Xiao Li, Yanpeng Li, Yang Xu, Yixuan Wang, Huaqing Zhang, Rui Men, Bochao Mao, Chengruidong Zhang, Fan Zhou, Hao Luo, Haofeng Huang, Haoran Lian, Haoyan Huang, Hongqing Chen, Jianwei Zhang, Jing Xu, Junjie Wang, Langshi Chen, Liangyu Wang, Linlang Jiang, Man Yuan, Minmin Sun, Peng Jin, Siqi Zhang, Siyu Wang, Xingzhang Ren, Yakai Wang, Yi Zhang, Yiming Dong, Yizhong Cao, Yubo Ma, Yunfei Mao, Bo Zheng, Dayiheng Liu
Computation and Language
Beyond Token-Level Guidance: Inference-Time Alignment of Specialized LLMs via Cross-Family Representation Steering
Jin Gan, Xin Li and Jun Luo
Large
language models (LLMs) finetuned for specialized domains represent crucial high-impact applications. Inference-time
alignment improves safety degraded from specialization finetuning without requiring substantial computational resources, complementing finetuning-based methods with an easy-to-use, plug-and-play solution. However, existing inference-time methods fail to reliably improve safety without disrupting domain capability. We identify the root cause as complementary expertise orthogonality: specialized base models and general-domain guidance models have orthogonal competencies, making the guidance signal unreliable for specialized generation. This primarily manifests as stop
token interference, where the guidance model's tendency toward continuation overrides the base model's decision to stop, burying correct answers under guidance-induced continuation. To address this problem, we propose CREST, an inference-time
alignment method that steers base model hidden representations using safety directions extracted from a guidance model of any family, avoiding
token-level structural limitations entirely. CREST improves safety where specialization has weakened it while preserving both domain-specific capability and the safety of already well-aligned models, outperforming baselines by up to 22.2% on safety
benchmarks. Our code is available at: https://github.com/DecayingSeart/CREST.
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Systems and Control
Converse Barrier Certificates for Set-Based Stochastic Reach-Avoid Verification
Bai Xue and C.-H. Luke Ong
Recent work established sufficient and necessary barrier-like conditions for infinite-horizon reach-avoid verification of
stochastic discrete-time systems from a single initial state. Whether such a converse characterization extends to a set of initial states, however, remains open. In this paper, we answer this question affirmatively for compact initial sets. We consider a uniform reach-avoid specification requiring the reach-avoid probability to exceed a prescribed threshold for every initial state in a compact set. Under appropriate assumptions, including continuous system transitions, together with a strict uniform probability margin, we extend the pointwise converse characterization to the uniform setting.
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Machine Learning
Online Estimation of Dynamic Origin-Destination Matrices Using Reinforcement Learning with Link-Flow Propagation Guidance
Donggyu Min and Dong-Kyu Kim
Dynamic origin-destination (OD)
matrix estimation calibrates time-dependent input demand for simulations to reproduce observed link flows. Reinforcement learning is well suited to online estimation because a trained policy estimates demand in a single evaluation, but varying target flows complicate learning from aggregate rewards. We propose reinforcement learning with link-flow propagation guidance (LFPG-RL), combining simulated vehicle propagation records with downstream errors to guide individual OD components. Using 250 weekday link-flow trajectories from a Melbourne arterial network, LFPG-RL achieves a mean test root mean squared error (RMSE) of 7.41 vehicles per 15-minute interval, mean absolute percentage error of 31.24%, and Pearson correlation of 0.987. Comparisons with optimisation, filtering and reinforcement learning without guidance show a 40.1% RMSE reduction over the strongest baseline, gradient descent with propagation guidance. LFPG-RL enables rapid demand updates that keep traffic simulations consistent with observed conditions, supporting the evaluation of traffic management strategies.
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Computer Vision
Knowing Beyond the Known: Reinforced Knowledge Specification for Multi-Label Class-Incremental Learning
Aoting Zhang, Dongbao Yang, Chang Liu et al. (+3 more)
Existing class-incremental learning methods struggle in multi-label scenarios (MLCIL) due to the inherent contradiction of learning objectives arising from co-occurring and incomplete labels. We argue that the core obstacle is the model's ambiguous boundary between known and unknown knowledge, which undermines historical knowledge retention, complicates current task learning, and limits adaptability to future concepts. To address this, we propose KBK (Knowing Beyond the Known), a reinforced knowledge specification framework that explicitly models what is known or not to unify historical, current, and prospective learning. Specifically, to clarify known knowledge, we develop a hierarchical feature purification module that disentangles fine-grained class-specific features from global features, where high-level semantic abstraction is reinforced with low-level visual features. Additionally, an uncertainty-aware recall enhancement strategy suppresses unreliable predictions based on distribution priors, improving the quality of historical recall. For probing the unknown, KBK leverages semantic correlations to synthesize informative unknown features under co-occurring, preserving
embedding space for future learning. Furthermore, to mitigate heterogeneous forgetting, we design a category-balanced gradient compensation loss that dynamically reweights gradient backpropagation according to forgetting speeds. Experiments on multiple
benchmarks validate the effectiveness and robustness of KBK, which surpasses prior best methods by 2.7% in Avg. Acc on MS-COCO B0-C10 setting even without any replay buffers.
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All authors Aoting Zhang, Dongbao Yang, Chang Liu, Xiaopeng Hong, Can Ma, Yu Zhou
Machine Learning
Context Staircase: Signature-Aligned Dynamics of Token Embeddings under Small Initialization
Junjie Yao, Liangkai Hang and Zhi-Qin John Xu
Token embeddings are the basic representational units that connect discrete
tokens with continuous computation in
language models. Although modern
language models learn
embeddings from random initialization through gradient-based training, the dynamical mechanism by which meaningful
embedding structures emerge remains unclear. In this work, we identify that the evolving
embedding structures are closely related to
token-conditioned label and contextual distributions, which we formalize as probability signatures. We observe a progressive learning process, which we term Context Staircase:
embeddings learn the low-order statistic signatures of the data before the high-order ones. More specifically, we observe that early in training they align with the simplest, context-free signature linking a
token to its label, and as training proceeds, they progressively reflect signatures involving more and more context
tokens. We then analyze the gradient flow of
embeddings under small initialization to explain this phenomenon, deriving
embedding evolution equations for feed-forward and self-
attention architectures. We further extend these observations to real
language-model training. Finally, we show that these
embedding structures play an important role in both task learning and the incorporation of semantic structure into the
embedding space. Overall, our results provide a dynamic explanation of how data statistics and architecture jointly shape
token embeddings in
language models, and reveal an implicit bias in the space of data statistics: training proceeds from simpler, low-order statistical relations toward increasingly complex, context-dependent ones.
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Human-Computer Interaction
One AI Signal, Many Human Judgments: A Bayesian Cascade Analysis of AI-based Credibility Indicators in Online Information Spread
Zhuoran Lu, Weilong Wang, Yangyang Yu et al. (+4 more)
Social media platforms increasingly use AI-based credibility indicators to help users judge misinformation. Unlike individual human-AI decision-making, these indicators are embedded in information spread: users see both an AI prediction and earlier judgments shaped by the same AI, and their own judgments may then enter the public history. Yet how to analytically characterize this process remains under-explored. We therefore introduce a social-learning lens for this setting by extending the classical
Bayesian cascade model with the AI indicator as a shared public signal. The resulting Gateway condition compares the evidence from the AI prediction with users' private impressions. Through this view, we show that AI changes what public history means. Crowd agreement may reflect accumulated independent human evidence, or repeated dependence on the same AI prediction. This creates a preservation-correction trade-off: stronger reliance on AI can preserve correct predictions, but can also lock in incorrect ones by blocking corrective private impressions. We calibrate the model using human-subject data on news veracity judgments. Although the AI outperforms human users, the average user weights it below her own impression but above several peer judgments, while individual users vary from discounting the AI to relying on it enough to cascade. Simulations show that over-reliance on a weak AI is especially harmful, and that diversifying AI signals across users can better keep the crowd informative. We conclude with implications for understanding human-AI interaction in information spread and designing misinformation interventions.
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All authors Zhuoran Lu, Weilong Wang, Yangyang Yu, Xinru Wang, Zhuoyan Li, Zhiwei Liu, Sophia Ananiadou
Machine Learning
Tail-Replay: Escaping the Curse of Linear Attention in Prefix Caching for Hybrid LLMs
Yirui Liu, Ruoling Qi, Xuaner Wu et al. (+2 more)
Hybrid large
language models interleave full-
attention layers with linear-
attention layers to reduce the cost of long-context inference. This structure complicates prefix caching: full-
attention key-value caches are
token-addressable, whereas linear-
attention layers maintain recurrent states that cannot be rolled back to arbitrary prefix boundaries. Existing hybrid prefix caching methods address this mismatch by storing recurrent-state checkpoints. As a result,
token-level matches are directly usable only at positions aligned with stored checkpoints, constraining prefix reuse to a discrete set of boundaries. We present Tail-Replay, a prefix caching mechanism that enables unconstrained
token-level prefix reuse in hybrid large
language models. The key insight is that linear-
attention mechanisms such as Gated DeltaNet can be viewed as a structured, lossy compression of the input prefix: gated recurrent updates progressively attenuate the contributions of earlier inputs. Consequently, the recurrent state of a matched prefix can be well approximated by replaying only a short, recent suffix of that prefix. Tail-Replay exploits this property by caching the exact full-
attention key-value cache while omitting recurrent-state checkpoints. On a cache hit, it reconstructs the linear-
attention states by replaying a short, recent suffix of the matched prefix. As a result, the reuse boundary is determined by the shared
tokens rather than by recurrent-state checkpoints. We evaluate Tail-Replay on three Gated DeltaNet-based hybrid models using the LongBench and RULER
benchmarks. With only a 5–10% replay budget, it retains 92.8–99.9% of full-prefill quality on LongBench and RULER. For serving efficiency, we evaluate time-to-first-
token speedups across multiple matched-prefix lengths—8K, 16K, and 32K. The speedup grows with prefix length, reaching $9.1$–$14.3\times$ over full prefill at 32K.
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All authors Yirui Liu, Ruoling Qi, Xuaner Wu, Penghang Liu, Jian Chen
Computer Vision
ScenePilot: Grow-and-Repair Policy for Text-Driven 3D Indoor Scene Generation
Jiawei Zhang, Hongsong Wang and Pan Zhou
Text-driven 3D indoor scene generation has advanced from
dataset-bound layout modeling to open-vocabulary synthesis with large
language and
vision-
language models. Yet existing methods remain limited: one-pass generators often yield geometrically invalid layouts, heavy post-hoc optimization is costly and unstable, and
prompt-only planners lack reusable layout priors for functional grouping and object relations. We propose ScenePilot, a
retrieval-augmented Grow-and-Repair framework that formulates scene generation as prior-guided incremental growth with learned rectification. Given a
prompt, the Hierarchical
Retrieval-Augmented Planning (HRAP) module retrieves room-, group-, and anchor-level layout priors to support functional group planning. A text-driven base generator then inserts object groups sequentially, while the Reinforcement
Multimodal Repair (RMR) module performs lightweight local correction after each insertion and a final global repair after completion. To train this policy, we construct SceneReverse-17k, a repair-trajectory
dataset built by perturbing high-quality 3D scenes in position, rotation, and scale, then using inverse operations as executable rectification targets. The policy predicts structured move–rotate–scale actions from rendered views, scene state, retrieved priors, and edit history. By combining HRAP with RMR, ScenePilot offers an efficient alternative to one-shot generation and heavy full-scene optimization, improving physical plausibility, functional coherence, and controllability while preserving diversity.
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Computer Vision
Learning to Restore More: Continual Capability Expansion for Pretrained Image Restoration Models
Hu Gao, Yulong Chen and Lizhuang Ma
Image restoration models are typically trained with a fixed set of capabilities. When new restoration requirements emerge, existing solutions usually train additional models or jointly retrain the original model with both new and historical data. Instead of designing another restoration backbone, we investigate how a trained restorer can continually acquire new capabilities without forgetting those learned previously. We propose RestoreMore, a continual capability-expansion framework that preserves the pretrained restoration model as a frozen capability anchor and learns residual expansion modules for newly arriving degradations. RestoreMore introduces a capability-oriented bi-level routing mechanism at multiple feature stages. The first routing level identifies restoration capabilities relevant to the current input, while the second selects and combines a sparse set of complementary degradation experts. This design enables newly introduced tasks to selectively reuse historical restoration knowledge and progressively enriches the expert bank available for subsequent restoration tasks. Extensive experiments on a wide range of restoration
benchmarks demonstrate that RestoreMore consistently acquires new restoration abilities while preserving and improving previously learned capabilities.
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Computation and Language
Lazy Grounding: Attacking Search Agents with Factual Evidence
Yulin Zhang, Yukun Huang, Sanxing Chen et al. (+4 more)
Search
agents mitigate
hallucination by grounding their answers in retrieved web results. However,
retrieval-based approaches also introduce an attack surface:
agents may cite misinformation from poisoned search corpora containing false or malicious documents. We demonstrate that, in some cases, search
agents'
reasoning and responses may be steered by completely factual but distracting information. We refer to this failure as lazy grounding. We expose lazy grounding by injecting nearby evidence from answer-changing rewrites of
benchmark questions into the search corpora. Each document contains factual evidence that supports a neighboring rewritten question but is retrieved for the original question. Across 12 model-
benchmark pairs, the attack causes the accuracy of search
agents' responses to drop by 5.9 points on average and by up to 17.3 points, while inducing nearby-answer adoption in every setting. The effect is even stronger when nearby evidence appears later or is more answer-shaped. Our results show that robust search
agents must defend against not only misinformation but also the misapplication of factual evidence. The code is publicly available at https://github.com/frankyzha/lazy-grounding.
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All authors Yulin Zhang, Yukun Huang, Sanxing Chen, Tianyi Lin, Ziang Yang, Xunjian Yin, Bhuwan Dhingra
Robotics
Data-Centric Neuromotor Interfaces for Portable Human-Machine Interaction
Jiaxuan Li, Di Wu, Jianhua Liu et al. (+7 more)
Dexterous human-machine interaction requires intuitive and expressive interfaces that can be efficiently deployed on constrained edge devices. Flexible material-based neuromotor interfaces hold considerable promise, as they decode human movement intention into natural control. Although emerging flexible electronic skins enable wearable high-fidelity data acquisition, practical deployment inevitably involves trade-offs between computational resources and portability. We present a data-centric paradigm where physiological features yield fundamental separability, providing sufficient discriminative cues for recognition. A wireless, high-bandwidth system developed for collecting various electrophysiological signals, when integrated with muscle-specific electrodes, forms a surface electromyography-based interface. Exploiting highly separable data, a 2,210-parameter model achieves 94.36% accuracy across 34 gestures and can be rapidly deployed on edge devices, establishing a new thousand-parameter
benchmark for dexterous decoding. The underlying data-
algorithm interactions in the data-centric paradigm are further clarified, demonstrating its feasibility in real-world scenarios. This study provides a principled and validated pathway for practical deployment of reliable neuromotor interfaces.
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All authors Jiaxuan Li, Di Wu, Jianhua Liu, Yuxin Zhao, Jinnuo Li, Xiao Zhang, Zhenzhi Ying, Changsheng Dai, Xiang Li, Liming Shu
Software Engineering
Update from Hell: Can Coding Agents Survive Hidden Breakage in Dependency Upgrades?
Zijian Luo, Runzhi He, Pengfei Gao et al. (+6 more)
Modern software systems rely heavily on third-party dependencies, but upgrading those dependencies remains a costly maintenance activity. Dependency upgrades do not always preserve the function signatures, type systems, APIs, or runtime semantics assumed by existing code. Consequently, developers often need to perform source code adaptations to accommodate dependency-induced changes. However, such code-level changes are often not explicitly communicated to project maintainers, posing a significant challenge to software reliability. Meanwhile, coding
agents have emerged as a new form of software development tool and are increasingly adopted by developers due to their automation capabilities. In this paper, we introduce DEPBENCH, a
benchmark consisting of 203 real-world dependency-upgrade tasks across five package ecosystems spanning five
language communities, each involving hidden code-level changes that require source code adaptation. We evaluate mainstream coding
agents on DEPBENCH. The best completed configuration solves only 104/203 tasks (51.2%), with substantial variation across
agent harnesses, models, and ecosystems, highlighting an important gap between current
agent capabilities and real-world software maintenance needs.
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All authors Zijian Luo, Runzhi He, Pengfei Gao, Yu Kang, Zeqi Lin, Minghua Ma, Qingwei Lin, Saravan Rajmohan, Yongqiang Tian
Data Structures and Algorithms
Beating Quadratic Time–Message Trade-off in Distributed Minimum Spanning Tree Construction
Taisuke Izumi, Naoki Kitamura and Toshimitsu Masuzawa
We present a new distributed
algorithm for computing a minimum spanning tree (MST) in the \textsf{CONGEST-KT$_{1}$} model, where messages are limited to $O(\log n)$ bits and each vertex initially knows the identifiers of its neighbors. Our
algorithm exposes a two-parameter time–message trade-off: for any $0 \leq \lambda \leq \kappa \leq 1/2$, it runs in $\tilde{O}(n^{\lambda}D_G + n^{1 - \kappa - \lambda} + n^{1 - 2\kappa + \lambda} + n^{1/2})$ rounds and uses $\tilde{O}(\min\{m, n^{1 + \kappa}\})$ messages, where $n$, $m$, and $D_G$ are the number of vertices, edges, and thenetwork diameter, respectively. In particular, setting $(\kappa, \lambda) = (1/3, 1/6)$ yields an MST
algorithm running in $\tilde{O}(n^{1/2} + n^{1/6}D_G)$ rounds with only $\tilde{O}(n^{4/3})$ messages. Under the mild assumption $D_G = O(n^{1/3})$, this is round-optimal while improving the best known message bound of $\tilde{O}(n^{3/2})$. More broadly, our
algorithm breaks the quadratic time–message trade-off barrier $\mathrm{\# rounds} \cdot \mathrm{\# messages} = \tilde{\Omega}(n^2)$, which no previous MST
algorithm in the \textsf{CONGEST-KT$_{1}$} model has been able to overcome, and it does so for almost the entire range of the diameter $D_G$. As a byproduct, we also obtain new low-message broadcast, spanning-tree, and leader-election
algorithms.
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Networking · Cross-listed from physics.optics
Sensitivity Comparison of Microwave-Frequency and Optical Fibre Interferometry Based on State-of-the-Art Components
Georgios Aias Karydis, Marco Fasano, Paola Parolari et al. (+3 more)
We compare the sensitivity of fibre interferometers to vibrations using microwave oscillators and state of the art
lasers over distances up to 70 km. High spectral purity
lasers outperform only above 10 Hz, highlighting microwave advantages at low frequencies and the potential of hybrid microwave optical interferometry systems
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All authors Georgios Aias Karydis, Marco Fasano, Paola Parolari, Pierpaolo Boffi, Charis Mesaritakis, Adonis Bogris
Computation and Language
AIA$^{2}$: Attribute-Agnostic Imbalance Augmentation for Subgroup Robustness
Hanshu Rao, Guangzeng Han and Xiaolei Huang
Attributes describing data content and context can induce diverse imbalance patterns that go beyond label imbalance alone. However, existing studies primarily address label imbalance while overlooking data attributes, such as topics and demographics, which can induce meaningful subgroup structure while causing model degradation on underrepresented subgroups. We propose Attribute-Agnostic Imbalance Augmentation (AIA$^{2}$), a framework for improving model robustness under varying subgroup imbalances without explicit subgroup annotations. AIA$^{2}$ automatically discovers varying imbalances via latent semantic distributions, obtains slices with both learning difficulty and subgroup imbalance deficits, and deploys a large
language model (LLM) for subgroup-aware imbalance augmentation. We have evaluated AIA$^{2}$ on 5 popular corpora with rich domains and their attribute values, covering social issues and diverse topics. Results show improved performance on the lowest-performing subgroups and consistent gains over competitive baselines. Ablation studies confirm complementary contributions from each component, and additional analyses show that AIA$^{2}$ provides a practical and consistent way to improve worst-group robustness under data subgroup imbalance. Code is available at https://github.com/trust-nlp/AIA2-Subgroup-Robustness.
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