Artificial Intelligence · Cross-listed from cs.AR
ARES: Adaptive Reasoning-Effort Steering for PPA- and Cost-Aware RTL Optimization with LLM Agents
Stef Cuyckens, Mihaela Jivanescu, Jun Yin et al. (+2 more)
Large
language model (LLM)
agents optimize the power, performance, and area (PPA) of register-transfer-level (RTL) designs by iterating over edits, synthesis, and PPA analysis, paying a dollar cost for every LLM call. Prior
agents report the quality reached without its normalized cost, attribute that quality to an engineered cross-design
memory, and hold the
reasoning effort of every call fixed. We propose Ares with three corresponding innovations. (1) We introduce a normalized dollar cost per LLM call reported alongside the figure of merit (FoM), enabling fair comparison across effort levels and optimizers. (2) Using this accounting, we find the construction of the long-term
memory matters little. An engineered
memory brings no dependable gain over a plain concatenation of the same experience. (3) We instead adapt the per-call
reasoning effort by escalating to deeper
reasoning only once progress at a lower effort stalls, via a patience counter fit on 21 training designs, allocating
reasoning where it pays rather than uniformly across all iterations. On three test designs unseen during training, the effort policy lowers the FoM by 22-28% with Opus 4.6 and by 18-34% with GPT-5.5, where the best fixed effort reaches 20-24% and 19-29%, respectively, at equal normalized cost. Ares closes on average 91% of the gap from an LLM-drafted multiply-accumulate unit to its highly hand-optimized counterpart. It also reaches a 5% lower FoM than state-of-the-art Dr. RTL at only 35% of its cost.
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All authors Stef Cuyckens, Mihaela Jivanescu, Jun Yin, Chao Fang, Marian Verhelst
Artificial Intelligence
An Empirical Study of Coordination Mode as the First-Class Citizen in From-Scratch Multi-Agent Coding
Yanyu Ren, Yunfeng Bai, Xizheng Wang et al. (+2 more)
Multi-
agent vibe coding promises to accelerate software development, yet existing
benchmarks rely on synthetic environments that ignore practical time and monetary costs, conflate
reasoning with communication, and reward only superficial completion. We introduce multi-
agent from-scratch evaluation
benchmark, MSEval, evaluating multi-
agent coding on real-world tasks. Grounded in 10 authentic, full-stack projects across 10 domains, MSEval scores performance using hierarchical requirements and deterministic rubrics. Its execution engine, LegoGent, tests 10 collaboration
topologies where
agents coordinate via periodic sync intervals and deploy through native CI/CD pipelines. Concurrently, the automated grader TAgent dynamically probes implementations to jointly measure functional success, latency, and prefix-cached
token cost. Across 100 runs, MSEval reveals that organizational
topology rivals model capability in shaping the speed–cost–quality trade-off. For identical tasks and models, varying the
topology shifts scores by over 30 points and doubles wall-clock time. Structured pipelines converge fastest with the highest quality, whereas heavy managerial oversight degrades performance. Ultimately, MSEval establishes a rigorous, reproducible standard for measuring how multi-
agent teams actually build software. The
benchmark is released at https://github.com/robinren03/MSEval.
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All authors Yanyu Ren, Yunfeng Bai, Xizheng Wang, Li Chen, Dan Li
Artificial Intelligence
Search as Computation Allocation
Alexander Tuisov
Many
algorithms spend an internal resource before returning a decision and are evaluated only by the quality of that terminal output. We formalize such procedures as terminal computation-allocation problems: costly computations produce observations, update beliefs about a latent environment, and matter only through terminal decision loss. Bellman equations characterize optimal allocation under fixed budgets, priced computation, and exact certification. We then relate value of computation (VOC) to information. Mutual information equals myopic VOC under log loss, whereas under simple regret VOC is a knowledge-gradient quantity; moreover, information gain can rank computations arbitrarily poorly, although it gives a one-sided upper bound on VOC. Bandit pulls, tree simulations, and node expansions illustrate the same model under different computation
topologies. Finally, under an explicit frontier-resolution and heuristic-error model, maximizing approximate VOC recovers weighted A*, with A* and greedy best-first search as limiting cases. The theory identifies a shared decision problem without asserting that one acquisition rule is universally optimal.
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Artificial Intelligence
Orca: Neural Operators for Causal Reasoning in Continuous Time
Gerrit Großmann, David A. Selby and Sebastian J. Vollmer
Structural causal models are the standard
language for
reasoning about interventions and counterfactuals, but they describe static variables, typically measured once, and usually forbid cyclic dependencies. Many systems we care about, such as patients, climates, and economies, instead evolve continuously in time, are observed at irregular time points, and contain feedback loops. We argue that neural operator learning provides a natural foundation for causal
reasoning in this setting, and propose Orca, a framework in which each node of the causal graph is a function of time and each mechanism is a learned map between function spaces. We extend existing neural operator architectures to express causal mechanisms: a mechanism computes the function value of a node from its parent nodes by taking several parent functions as input, respects the arrow of time, and treats latent exogenous noise as a function that can be inferred and reused for counterfactuals. We formalize the model class and demonstrate counterfactual
reasoning on synthetic continuous-time examples. Code is available at https://github.com/gerritgr/orca
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Artificial Intelligence
Back to All-Entity Ranking: Sampler-Dependent Evaluation in Continuous-Time Dynamic Graphs
Minwoo Yu and Young-guk Ha
Next-destination prediction in continuous-time dynamic graphs (CTDGs) commonly ranks an observed interaction against sampled negative destinations. The resulting score is conditional on both the negative distribution and the number of candidates chosen by the researcher. We show that a non-uniform negative distribution changes the Bayes-optimal ranking, while even a finite candidate set drawn uniformly can destabilize model rankings and measured module effects. Time-varying source-destination history membership and model operations that use this information directly transmit the sampler's influence to the evaluation score. We examine this mechanism using a factorial evaluation of repeated and new positives against seen and unseen negatives, a minimal scorer based solely on pair-history membership, and controlled representation interventions. Across six models on LastFM, MOOC, Reddit, and Wikipedia, at least one model pair changes relative order between the expected Uniform-20 metric and the full catalog on three of the four
datasets. The measured effect of the same module also changes in magnitude and direction with the candidate-set size and training objective. These results establish that model-superiority and ablation conclusions from sampled-negative
benchmarks are conditional on the stated candidate configuration. All-entity ranking evaluates every destination in a fixed catalog, eliminating negative-selection freedom and sampling variation while retaining the original CTDG scorer. We therefore recommend all-entity ranking as the primary evidence for architecture comparisons on CTDG
benchmarks with an enumerable, fixed destination catalog.
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Artificial Intelligence · Cross-listed from cs.CV
EEG-EditBench: Probing Visual Information in EEG-Image Retrieval Models with Controlled Image Edits
Kaifan Zhang, Lihuo He, Yuqi Ji et al. (+4 more)
Recent
EEG-to-image
retrieval models have achieved strong performance in identifying viewed images from semantically diverse candidates. Yet such success does not reveal what visual information supports the match. A model may readily identify a cheetah among tools, plants, and vehicles, but can it still distinguish the viewed cheetah from the same scene with the cheetah replaced by a dog? Motivated by this question, we introduce
EEG-EditBench, a diagnostic
benchmark that examines this question through controlled edits of object identity, attributes, background, and object presence. Built from the 200 THINGS-
EEG2 test images,
EEG-EditBench contains 2,137 quality-controlled edits and evaluates eight representative
EEG visual decoding models. Our results show that strong standard
retrieval does not consistently transfer to edit-based evaluation, with fine-grained attribute changes presenting the greatest challenge.
EEG-EditBench reveals model behavior hidden by aggregate
retrieval accuracy and provides a controlled basis for studying what visual information
EEG-image models preserve. The code and complete
dataset are publicly available.
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All authors Kaifan Zhang, Lihuo He, Yuqi Ji, Junjie Ke, Lukun Wu, Tianhao You, Xinbo Gao
Artificial Intelligence
Simplifying Neural Networks During Training
Lorenzo Sciandra, Samuele Fonio and Roberto Esposito
Understanding and exploiting the training dynamics of overparameterized deep neural networks remains a central challenge in modern machine learning. Recent evidence on Neural Collapse (NC) shows that class representations and classifiers exhibit highly structured geometry, while the Tunnel Effect suggests that only a subset of layers is essential for feature extraction. We combine these two perspectives and propose an NC-inspired training framework for simplifying deep networks during training. Our method monitors representation dynamics through the Inverse Fisher Criterion, a stable and efficient proxy for the variability collapse behavior, to identify both the split point between feature extraction and classification and the training stage at which simplification becomes viable. We then replace the trailing layers with a lightweight classification head and continue training the reduced model. Experiments on image-classification
benchmarks across MLP, VGG, and ResNet architectures show that the proposed method achieves substantial parameter reductions while maintaining accuracy comparable to that of the full model. Code to reproduce the experiments can be found at: https://github.com/LorenzoSciandra/NNS.
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Artificial Intelligence · Cross-listed from cs.CL
FinanceHarness: Autonomous Financial Deep Research Framework
Yijia Xiao, Rujun Han, Yanfei Chen et al. (+8 more)
Powered by advances in LLMs and autonomous
agents, deep research has become one of the most widely adopted agentic products. However, most deep research systems write general-purpose reports, which are inadequate for financial deep research. Financial research demands specialized knowledge to analyze historical patterns and forecast upcoming events. Automating financial deep research therefore requires both a layered harness to drive the research
agent and a verifiable, point-in-time
benchmark that prevents leakage of future information. We present FinanceHarness, a harness that runs finance-oriented tools and practitioner-guided workflows, automating financial deep research end to end: environment and data construction, the
agent execution loop, and reward modeling. We further propose FinanceGym, comprising thesis-driven research questions and rubrics that combine pre-cutoff and post-cutoff criteria. Professional expert validation yields an 82% pass rate. With the same open-weight backbone, FinanceHarness improves the overall rubric score from 25.3% to 32.4%, demonstrating the effectiveness of our specialized harness design. However, even pairing FinanceHarness with the most cutting edge LLM (e.g. Opus-5), the FinanceGym score is below 45%, showing that it is a challenging
benchmark for financial deep research. Leaderboard is available at: https://financegym.github.io/ and FinanceHarness code is available at: https://github.com/Yijia-Xiao/FinanceHarness.
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All authors Yijia Xiao, Rujun Han, Yanfei Chen, Zifeng Wang, Ke Jiang, Zhongying CuiZhu, Vishy Tirumalashetty, Wei Wang, Burak Gokturk, Tomas Pfister, Chen-Yu Lee
Artificial Intelligence · Cross-listed from cs.CL
AutoSupervision: Closing the Feedback Loop in Scientific Workflows with Grounded Revision Verification
Haobo Li, Eunseo Jung, Wenxiao Zhao et al. (+8 more)
Recent advances in large
language models (LLMs) have enabled AI systems to assist scientific research and peer review. However, an essential capability for reliable AI-assisted scientific workflows remains underexplored: verifying whether reviewer feedback leads to meaningful and evidence-supported manuscript improvements. We introduce AutoSupervision, which evaluates whether scientific manuscript revisions genuinely address reviewer concerns through grounded evidence. AutoSupervision leverages transparent peer-review records as a natural source of supervision, where reviewer comments specify scientific concerns, author responses describe claimed resolutions, and revised manuscripts provide evidence of changes. Given reviewer comments, author responses, and revised manuscripts, models must characterize reviewer concerns, determine whether concerns have been addressed, and identify supporting manuscript evidence. We construct AutoSupervision from 56,000 Nature Communications articles and corresponding review records. Then we conducted experiments on LLMs, the ablation study, and the case study. Our results show that while LLMs perform well in characterizing reviewer concerns, with GPT-5.5 achieving a score of 0.754, evidence-based verification remains the primary bottleneck, with the best-performing model reaching only 0.501.
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All authors Haobo Li, Eunseo Jung, Wenxiao Zhao, Feng Liu, Jiong Wang, Kaiyi Xu, Zijie Guo, Zixin Chen, Ben Fei, Fenghua Ling, Lei Bai
Artificial Intelligence
Virtual Process Dossier: A Process-Aware Data Catalogue
Lukas Kubelka, Alexander Bott, Frank Döhner et al. (+6 more)
We propose the Virtual Process Dossier (VPD), a Knowledge Graph-based data catalogue that also captures workflow provenance. We developed VPD for multi-stage manufacturing use-cases where downstream AI-based optimization tasks require to distinct between
datasets generated during individual workflow steps. VPD provides these
datasets in a FAIR manner and makes both prospective and retrospective workflow provenance explicit. Our contributions are: (1) the VPD ontology that serves as the catalogue's semantic core; (2) the VPD provenance framework that integrates ontology instantiation into the production environment; and (3) the VPD user interface that provides human-centered interaction with the VPD Knowledge Graph. The ontology and code are available at https://github.com/kubeluk/VirtualProcessDossier .
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All authors Lukas Kubelka, Alexander Bott, Frank Döhner, Saksham Kiroriwal, Georg Zeeb, Julia Butte, Julius Pfrommer, Jürgen Beyerer, Tobias Käfer
Artificial Intelligence
Crossing the Margin Cliff: Toward Relearn-Robust LLM Unlearning via Margin Calibration
Xiangyu Yin, Jiaxu Liu, Zhen Chen et al. (+1 more)
Large
language model unlearning is consistently fragile under relearn attacks. On TOFU, fine-tuning on twenty forget examples substantially recovers held-out forget-set ROUGE for every method we evaluate, and we trace this fragility to optimization geometry. The per-
token answer margin of fourteen post-hoc methods spanning gradient, preference, and
distillation families converges into a narrow band above the retain reference in 41 of 42 method–size
cells, a regularity we call the margin cliff. We prove that this cliff follows whenever the retain coupling holds the diagnostic log-odds of forget content above a floor, a condition that
token-saturating losses induce at stationarity and that we verify directly on 34 of 42
cells. Margin Calibration (MC) is a plug-in polish adding a non-saturating margin hinge anchored at the reference's per-
token margin plus a KL probe on a disjoint instruction corpus, restoring forget-side pressure where the native loss saturates. Under a stated gradient-dominance condition, whose on-trajectory gradient signature we measure by instrumenting the polish, its stationary set lies on the cliff-crossing side, yielding an attack-budget upper bound on the relearn margin lift. Across TOFU (three Llama-3 sizes, three forget tiers), MUSE-News on Llama-2-7B-hf, and a Phi-3.5 panel, a single frozen configuration wins all 14 head-to-head forget aggregates and all populated relearn
cells (panel-mean post-attack ROUGE-L $0.41$ to $0.18$) and lowers raw membership AUC on 13/14, with reduced retain-side utility as the main cost. A deployment variant matches these gains without a retain-trained reference.
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All authors Xiangyu Yin, Jiaxu Liu, Zhen Chen, Chih-Hong Cheng
Artificial Intelligence · Cross-listed from cs.CV
Harshit Mittal and Arash Rabbani
Accurate
cell segmentation and classification are foundational to digital pathology, enabling quantitative tissue analysis for diagnosis and treatment planning. Encoder-decoder architectures that fuse multi-scale features through skip connections have become the dominant paradigm for this task, yet standard direct skip connections treat every spatial location equally, which leads to redundant and potentially conflicting information reaching the decoder. To overcome this problem, various gating mechanisms have been introduced, but most of them operate solely on filtering encoder information, neglecting the benefit of global contextual information from the decoder. This study proposes replacing conventional skip connections in a CellViT-based model with a novel Spatial
Attention Fusion (SAF) Gating module. Each SAF gate concatenates the encoder skip and upsampled decoder features, compresses them through two pointwise convolutions with an intermediate ReLU, and applies a channel-wise softmax to produce a per-pixel "heatmap of trust" that sums to unity at every spatial location, allowing the network to learn where each source is most trustworthy. The resulting fused features improve the model's ability to detect the minority "Dead" class, which in turn enhances the multi-class panoptic quality (mPQ) on the PanNuke
dataset. SAF Gating is compared against six gating alternatives including no gating,
attention gates, squeeze-and-excitation, CBAM, cross-
attention, and attentional feature fusion on PanNuke and MoNuSeg
datasets. SAF Gating achieves the highest mPQ (0.471), a gain driven primarily by a 14.5-point improvement in Dead-class F1 score compared to ungated CellViT baseline.
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Artificial Intelligence
MemTxn: A Transaction Boundary for Source-Supported Updates and Complete-State Recovery in Agent Memory
Hanshuai Cui, Zhiqing Tang, Zhi Yao et al. (+3 more)
Persistent
memory lets long-running large
language model
agents reuse information across sessions and tasks. Yet errors in writable
memory can persist and corrupt future behavior. Existing systems improve storage and
retrieval, but they do not provide a transaction boundary for reliable updates and recovery. We therefore propose MemTxn, a governance layer outside the answer model. MemTxn verifies whether an update is supported by its source. It also selects the visible version when facts conflict and restores the application-visible state after a fault. The system uses Ordered PatchTest to validate writes, a Temporal Resolver to select versions, and a durable snapshot journal to recover state. On an item-disjoint audit, MemTxn accepts all 60 supported originals and rejects all 179 hard negatives. Under persistent multi-key faults on LongMemEval-S and LoCoMo states, it restores the complete declared active map without knowing the actual physical write set. On MemoryAgentBench FactConsolidation, MemTxn achieves the highest average F1 across all twelve answer-model configurations. It outperforms Dense by 17.06–24.07 points in five representative settings.
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All authors Hanshuai Cui, Zhiqing Tang, Zhi Yao, Fanshuai Meng, Qianli Ma, Weijia Jia
Artificial Intelligence
Sign Language Question Answering: A New Task, Benchmark, and Baseline for Sign Language Understanding
Shiwei Gan, Lichen Wang, Xiao Liu et al. (+4 more)
Recent advances in sign
language (SL) understanding (SLU) have led to remarkable progress in tasks such as continuous SL recognition and SL translation. However, these tasks are designed with predefined objectives, requiring models to learn a fixed mapping from sign
videos to glosses or spoken-
language sentences. As a result, they provide only a limited assessment of whether a model truly understands the semantic content of SL
videos. To address this limitation, we first propose a new task, Sign
Language Question Answering (SLQA), which evaluates SL understanding by requiring models to answer arbitrary natural
language questions about SL
videos. Unlike previous SLU tasks, SLQA provides a more flexible and comprehensive evaluation framework that assesses multiple
reasoning capabilities beyond recognition and translation. To facilitate this task, we further construct two SignQA
benchmarks based on PHOENIX14T and CSL-Daily by automatically generating question-answer pairs from existing gloss and sentence annotations using carefully designed templates. The resulting
datasets cover five complementary question categories, including position
reasoning, structural
reasoning, visual search, gloss recognition, and translation understanding. Finally, we propose a simple yet effective baseline model equipped with a Question-Conditioned Modulated Temporal Downsampling module and an in-domain knowledge transfer strategy, enabling effective knowledge transfer from existing SLU tasks while enhancing question-aware temporal feature modeling. Extensive experiments demonstrate that our baseline consistently outperforms representative
vision-
language models across all question categories, establishing a strong
benchmark for future research on SLQA.
Datasets are available at:{https://huggingface.co/
datasets/hulala/SignQA-2026}.
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All authors Shiwei Gan, Lichen Wang, Xiao Liu, Yafeng Yin, Kuizhuang Liu, Sanglu Lu, Lei Xie
Artificial Intelligence
STEREODISCO: Discovering Stereotypicality in LLMs
Farane Jalali Farahani, Corina Dima, Mojtaba Nayyeri et al. (+2 more)
LLMs encode, convey, and perpetuate stereotypes. Prior computational research focuses on a small set of semantic axes investigated in social psychology, and operates on word
embeddings produced by
language models, leaving open which other semantic axes carry stereotypical associations in LLMs and how LLMs internally represent such axes. We introduce STEREODISCO, a framework that adapts the semantic differential method (Osgood et al., 1957) to the systematic study of stereotypes in LLM internal representations. STEREODISCO constructs approx. 2,000 candidate semantic axes from WordNet antonym synsets, recovers each as a
geometric axis in the LLM's activation space via probing, and identifies stereotypical axes via a statistical test over concept projections. As a case study, we apply STEREODISCO to social group stereotypes with LLAMA-3-8B-INSTRUCT and MISTRAL-7B-INSTRUCT. We find that the two LLMs agree with each other on social group ratings more than with humans, suggesting that LLM-encoded stereotype content diverges from that documented in social psychology. We also discover stereotypical axes not investigated in prior work – including humble vs. proud, narrow-minded vs. broad-minded, and cowardly vs. brave, which human annotators independently confirm.
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All authors Farane Jalali Farahani, Corina Dima, Mojtaba Nayyeri, Raphael H. Heiberger, Steffen Staab
Artificial Intelligence · Cross-listed from cond-mat.mtrl-sci
Deep Learning for Accelerated Long-Horizon Forecasting of Multicomponent Multiphase Microstructure Evolution in High-Entropy Alloys
Hamidreza Razavi and Nele Moelans
Phase-field modeling provides a powerful approach for predicting microstructure
evolution but becomes computationally prohibitive for multicomponent and multiphase systems over large spatial and temporal scales. This work presents an AE-GCN-LSTM surrogate framework for long-horizon
forecasting of microstructure
evolution in the multicomponent AlCrFeNi high-
entropy alloy system containing coexisting BCC and FCC phases. A multi-head autoencoder compresses the four elemental concentration fields and phase-field order parameter into latent representations, which are formulated as graphs for learning their spatial and temporal
evolution. The framework accurately forecasts microstructure
evolution over horizons extending to 3,000,000 simulation timesteps. Its robustness is systematically evaluated under previously unseen conditions without retraining, fine-tuning, or parameter adaptation. These evaluations include variations in FCC precipitate size and initial position, microstructures containing one, two, and five FCC precipitates, and complex phase interactions involving precipitate merging and splitting. Although trained only on 100 x 100 computational domains containing a single nominal
alloy composition, the framework is successfully transferred to larger 256 x 256 and 512 x 512 systems and to previously unseen AlCrFeNi compositions. Across the evaluated configurations, the model preserves the dominant phase morphology and compositional
evolution while providing computational speedups ranging from approximately 7200 to 62300 relative to conventional phase-field simulations. These results demonstrate that latent graph-based AE-GCN-LSTM
forecasting provides a scalable and computationally efficient surrogate for long-horizon simulation of multicomponent, multiphase microstructures and offers a promising foundation for high-throughput
alloy design.
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Artificial Intelligence · Cross-listed from cs.CL
Beyond Borrowed Histories: Person-Aligned User Simulation for Interactive Role-Playing Evaluation
Yuhang Zhu, Mingxuan Du, Benfeng Xu et al. (+3 more)
Role-playing
agents (RPAs) have become one of the most important consumer applications of large
language models. Users engage in multi-turn conversations with RPAs for experiences such as emotional comfort, making reliable evaluation essential for measuring capability, comparing systems, and guiding further improvement. Existing
benchmarks, however, typically require an RPA to continue a fixed dialogue history and then evaluate the continuation using a fixed rubric detached from the user. We identify and empirically demonstrate two limitations of this design. First, an RPA's output is shaped by the preceding dialogue history, preventing a scientifically grounded assessment of its role-playing ability in real multi-turn settings. Second, user experience varies substantially across individuals, and conventional fixed rubrics need not align with user satisfaction. We therefore introduce PALATE (Person-Aligned LLM-Simulated-User Assessment with Tailored Evaluation), a scalable RPA
benchmark built on user simulators. PALATE is accompanied by a pool of 300 character profiles. Its main evaluation trains five per-user simulators and lets them engage candidate RPAs in free-form, multi-turn conversations over a pre-frozen panel of character profiles. Alongside a general quality rubric, we construct personalized rubrics to measure user satisfaction; on held-out annotated data, the personalized rubrics show higher agreement with human judgments than the general rubric. In the main evaluation of 16 candidates, PALATE separately characterizes generic turn quality, long-horizon session capability, and per-user experience on multi-turn trajectories co-constructed by each candidate. It thereby produces interpretable evaluations of specific user-RPA pairs rather than compressing systems into a single user-independent ranking.
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All authors Yuhang Zhu, Mingxuan Du, Benfeng Xu, Jie Gao, Lingyun Yu, Hongtao Xie
Artificial Intelligence
MemeBench: What LVLMs Miss When Interpreting Culture-Dependent Memes
Weihang Wang, Kainan Tu, Jielei Zhang et al. (+9 more)
Large
vision-
language models have improved at describing visual content, but accurate descriptions do not ensure interpretation when meaning depends on knowledge beyond the pixels. Memes expose this gap because they rely on cultural entities, background knowledge, and community conventions. Most meme
benchmarks reduce interpretation to labels or holistic scores, obscuring where an explanation breaks down. We introduce MemeBench, a diagnostic
benchmark of 1,253 Chinese and English memes with human-written references and quality-controlled VIKR annotations, centered on anime, comics, games, and adjacent online subcultures. Its VIKR schema decomposes explanations into Visual clues, Identity links, Knowledge units, and
Reasoning mechanisms. Across 26 LVLMs, every model covers visible content more reliably than the knowledge needed to interpret it, and even the strongest retains a 22.6% Visual-Knowledge gap. To test whether this diagnosis can guide improvement, we introduce KAR, an entity-guided
retrieval baseline built on CultureBase. Across four controlled models, KAR raises VIKR Success by 3.6-7.4% and, compared with generic
retrieval, repairs more answers and breaks fewer. Yet both
retrieval conditions improve Identity and Knowledge while reducing Visual coverage in every comparison. MemeBench reveals whether an interpretation succeeds, what is missing, and whether targeted evidence fills the diagnosed gap.
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All authors Weihang Wang, Kainan Tu, Jielei Zhang, Run Yang, Boheng Sheng, Yuchen He, Yu Xie, Pengyu Chen, Peiyi Li, Huyang Sun, Longwen Gao, Zhouhui Lian
Artificial Intelligence · Cross-listed from physics.hist-ph
Can AI Follow In Einstein's Footsteps?
Michael Shalyt, Nathan Regev, Marin Soljačić et al. (+1 more)
AI is accelerating physics discovery, but perhaps away from Einstein-level theory building. To understand this gap, we must recognize a striking trend: while being very successful, the most visible AI contributions to physics discovery appear to mirror the historical development of physics, but in reverse. Human discovery in physics progressed, in broad strokes, from ancient pattern prediction, through phenomenological laws such as Kepler's, to principle-based universal theories such as relativity and the Standard Model. On the AI side, prominent contributions to physics discovery point in the opposite direction: early milestones emphasized explicit equation-discovery methods, such as symbolic regression, whereas more recent frontier contributions are powerful predictors such as AlphaFold and GraphCast, which can be remarkably accurate yet do not provide clear theoretical understanding. If this trend continues, AI would become extraordinarily good at prediction but may struggle to ever propose its first serious contender to
quantum gravity or other paradigm-level theories. We review the current landscape of AI for physics discovery and highlight a critical missing skill: the ability to pose the right questions or invent the right principles to guide the development of new theories and the tests to falsify them. This mode of discovery has driven many of the deepest advances since the 17th century, where
symmetry, simplicity, and new mathematical frameworks guided theory construction before experimental tests. Equipping AI systems with such skills could move them from predicting within known frameworks to proposing the next paradigm-level discovery in physics.
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All authors Michael Shalyt, Nathan Regev, Marin Soljačić, Ido Kaminer
Artificial Intelligence
Annotating Topical Legal Insights from Case Proceedings
Subinay Adhikary, Dwaipayan Roy, Debasis Ganguly et al. (+2 more)
In this paper, we mainly concentrate on finding concepts or topics from the legal case proceedings, since adopting a structured representation for legal documents, as opposed to a mere bag-of-words flat text representation, can significantly enhance processing capabilities. To achieve this objective, we put forward a set of diverse concepts for legal case proceedings. With this motivation, we propose LeDA, a system for Legal Data Annotation. The system offers the generic functionality of annotating and adjudicating entities or concepts within documents via a web-based interface. A novel feature of our system is that it allows to dynamic create new tags for annotation, which is a particularly useful provision for situations where there exists no pre-defined ontology for the entities (concepts) that need to be annotated - these being rather discovered by annotators as they continue examining more documents. The system that we demonstrate is currently in use to annotate a set of concepts from legal documents to construct semantic representations of documents as bags of concepts that can then be used for several downstream tasks, such as prior case
retrieval, judgment prediction, and so on. Along with the system features in general, we also describe how LeDA was used by 3 assessors to annotate and adjudicate legal concept names from Indian Supreme Court case proceedings.
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All authors Subinay Adhikary, Dwaipayan Roy, Debasis Ganguly, Shouvik Kumar Guha, Kripabandhu Ghosh
Artificial Intelligence · Cross-listed from cs.CL
Semantic-Aligned Structural Abstraction for Multimodal Sentiment Analysis
Wei Chen, Junkai Li, Tongguan Wang et al. (+4 more)
Multimodal Sentiment Analysis (MSA) aims to interpret complex human
emotions by integrating natural
language with non-verbal modalities. Non-verbal modalities share a structural isomorphism with natural
language, as both can be viewed as feature sequences evolving over time. This isomorphism enables the transformation of non-verbal modalities into text-like
tokens for unified semantic
reasoning. Large
Language Models (LLMs), designed to understand and generate sequential data, can thus be utilized to interpret complex affective sequences. However, existing LLM-based methods primarily capture low-level superficial features, failing to model affective semantics arising from structural variations and contextual interactions. To address this limitation, we propose SentiLLM, a unified framework that leverages Semantic-Aligned Structural Abstraction to distill continuous raw signals into compact, semantically meaningful
tokens. Specifically, we introduce a Dual-Stream Salience-Context Calibration Mechanism, which disentangles non-verbal feature sequences into a focus stream and an ambient stream. The focus stream captures salient sentiment shifts (e.g., facial expressions) guided by textual priors, while the ambient stream characterizes stable background states. Through calibrating these dynamic sentiment shifts against background states, SentiLLM effectively projects non-verbal modalities into a unified semantic space, making them naturally understandable for LLMs. Serving as a plug-and-play module, SentiLLM significantly improves discriminative performance with only a small number of trainable parameters. Our method achieves superior performance on four
datasets, MOSI, MOSEI, CH-SIMS, and CH-SIMS v2, demonstrating the effectiveness of the structural abstraction paradigm in MSA. Our code is available at: \href{https://github.com/especiallyW/SentiLLM}.
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All authors Wei Chen, Junkai Li, Tongguan Wang, Hui Liu, Feiyue Xue, Chuanxiang Ma, Ying Sha
Artificial Intelligence
SpecCal: Ambiguity-Aware Candidate Calibration for Infrared Spectrum-Based Molecular Structure Reconstruction
Yixuan Chen, Bo Liu, Yusen Tan et al. (+3 more)
Inferring molecular structures from infrared (IR) spectra is a fundamental yet challenging problem. A key difficulty is that an IR spectrum provides limited structural information: different molecules may share similar functional groups and local vibrational patterns, leading to highly similar spectral responses. Thus, even when an observed spectrum has a unique underlying structure, reconstructing it from the spectrum remains ambiguous. Existing IR-to-molecule models usually generate a ranked set of candidate molecules, but this set is largely determined by the model's learned generation preference and may not fully capture the structures that best satisfy the observed spectral constraints. To address this limitation, we propose SpecCal, a training-free candidate calibration framework for IR-to-molecule prediction. SpecCal operates on the candidate outputs of existing base models and improves the prediction set by re-ranking current candidates while introducing additional structurally plausible alternatives guided by spectral consistency. The framework is plug-and-play and model-agnostic, requiring no parameter updates for integration with diverse base models. Experiments on multiple
benchmarks show that SpecCal consistently improves top-k reconstruction at both SMILES and scaffold levels across different base models. Further analyses demonstrate that calibrating candidate sets under spectral ambiguity provides a practical way to improve molecular reconstruction from IR spectra. The code is available at: https://anonymous.4open.science/r/SpecCal-B18A.
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All authors Yixuan Chen, Bo Liu, Yusen Tan, Guokun Yang, Wenjie Du, Jun Xia
Artificial Intelligence · Cross-listed from cs.LG
LoRA Scaffolded Policy Optimization (LSPO): A Sampling-Time Low-Rank Scaffold for Recovering Reinforcement-Learning Gradient on Zero-Reward Cliff Prompts
Ken Ding
Reinforcement learning from verifiable rewards (RLVR) for mathematical
reasoning suffers from a structural blind spot: on "cliff"
prompts-those on which every sampled rollout in a group fails-the group-normalized advantage is identically zero, so GRPO produces no gradient on precisely the
prompts at the frontier of the model's capability. We introduce LoRA Scaffolded Policy Optimization (LSPO), a sampling-time mechanism that recovers this lost gradient. Each RL step, LSPO detects cliff
prompts, fits a small low-rank (LoRA) adapter by a brief supervised step on their ground-truth solutions, re-rolls the cliffs with the base-plus-adapter model, splices the now-successful completions back into the RL batch with an importance-sampling correction, and takes a GRPO step on the base alone; the adapter receives only the supervised gradient and is discarded at checkpoint, yielding a base-only model. On DeepMath-103K with DeepSeek-R1-Distill-Qwen-1.5B, evaluated over n=5 paired seeds per arm at a matched 1000-step reporting horizon, LSPO's 5-seed mean matches or beats a DAPO baseline on all 16 (
benchmark, pass@k)
cells (15 strict wins and one exact tie), with gains of up to +10.7 points on AIME24/pass@4, +6.7 points on AIME24 and AIME26 at pass@16, and +2.4 points on MATH500/pass@1; averaged over the 16
cells the improvement is +3.8 points.
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Artificial Intelligence · Cross-listed from cs.CL
Reasoning Consensus: Structural Ensembling of LLM Reasoning via Weighted DAG Aggregation
Amruta Parulekar, Jinu Lee, Dilek Hakkani-Tür et al. (+1 more)
Large
Language Models (LLMs) reason in free-form chain-of-thought (CoT) without revealing which steps were well supported and which alternatives were rejected. In high-stakes domains, users then cannot judge if an answer rests on sound
reasoning. Majority voting and multi-
agent debate improve answers using model agreement, but do not expose agreement on individual steps.
Reasoning Consensus (RC) extracts a typed
reasoning graph from each CoT, merges equivalent steps across graphs from diverse models, and weights every step by how many chains attest to it. RC reveals this step-wise support and the varied justifications behind each answer. On 7
benchmarks across law, science, medicine, narrative
reasoning and logic, RC matches majority voting and debate in accuracy (69.4% vs. 69.0-70.3%). An LLM judge prefers RC's best-supported graph to other graphs for the same answer. In a user study, participants read RC graphs faster than debate transcripts or vote tallies, and 14 of 18 preferred them for future use.
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All authors Amruta Parulekar, Jinu Lee, Dilek Hakkani-Tür, Hari Sundaram
Artificial Intelligence · Cross-listed from cs.RO
RedFlow: Redirect Failure into Action-level Corrections for Flow-matching VLA Policy
Zhengyang Yan, Junhao Li, Fangqi Zhu et al. (+6 more)
Reinforcement learning (RL) can improve
Vision-
Language-Action (VLA) policies from deployment experience, but reward- and preference-based RL primarily identifies desirable behaviors without specifying how to correct failed actions, underutilizing failure trajectories and limiting sample efficiency. Can such corrections be derived from fixed rollouts? Our key insight is that rollouts with different outcomes may contain action chunks executed in similar states, enabling higher-quality chunks to provide locally supported corrective references. Building on this insight, we introduce RedFlow, an offline post-training method for flow-matching VLA policies. Execution-Context Matching groups chunks using a compact representation of estimated task progress and
robot proprioception. Quality-Guided Action Redirection assigns signed chunk-quality scores and aggregates higher-quality chunks into corrective targets, reinforcing high-quality chunks, suppressing low-quality chunks, and redirecting correctable chunks toward their targets. RedFlow requires neither external HIL corrections nor online data collection during post-training. Across four LIBERO suites, RedFlow improves average success from 56.2% to 68.2%, outperforming the strongest evaluated offline baseline, AWR (62.3%), by 5.9 points. Across three real-
robot tasks, it improves average success from 56.7% to 74.7%. On LIBERO-Spatial, RedFlow reaches 75.8% success with 1{,}536 fixed rollouts, while the evaluated online methods require 8.7–16$\times$ as many fresh post-training rollouts to reach the same threshold.
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All authors Zhengyang Yan, Junhao Li, Fangqi Zhu, Zijun Wang, Quanxin Shou, Yikun Miao, Xiaoyi Pang, Zicong Hong, Song Guo
Artificial Intelligence · Cross-listed from cs.SD
VocalRender: Score-Native Singing Voice Synthesis for Real-World Composition
Yukun Chen, Tianrui Wang, Zhaoxi Mu et al. (+2 more)
Existing singing voice synthesis systems often require predefined durations, explicit duration prediction, or time-aligned acoustic guidance, which limits their compatibility with practical composition workflows. We propose VocalRender, a score-native system that directly synthesizes singing from lyrics, pitches, symbolic note values, and tempo. It uses an interleaved lyric–note representation and an autoregressive
diffusion model to generate continuous acoustic latents while predicting the output length, eliminating the need for explicit duration prediction. Trained on a 2,300-hour singing
dataset, VocalRender achieves strong intelligibility, strong melody control, and high speaker similarity across both in-domain and out-of-domain
benchmarks. Notably, it outperforms the strongest baseline by $0.42$ points in naturalness CMOS, demonstrating the effectiveness of our proposed score-native architecture.
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All authors Yukun Chen, Tianrui Wang, Zhaoxi Mu, Xinyu Yang, EngSiong Chng
Artificial Intelligence · Cross-listed from cs.LG
Train Small, Deploy Large: Zero-Shot GNN Transfer Through Geometric Renormalization
Robert Jankowski, Pedro Almagro-Blanco, Marián Boguñá et al. (+2 more)
Graph neural networks (GNNs) can operate on large graphs but become infrastructure-sensitive at the scale of millions of nodes and typically require scalable training techniques for even larger graphs. This raises a central question: when can a model trained on a smaller, scaled-down replica of a graph be deployed on the full-resolution graph without retraining? We introduce a zero-shot transfer protocol in which a GNN is trained on a graph coarse-grained by
geometric renormalization (GR), and the resulting weights are transferred directly to the original network. Across synthetic and real-world networks, training on GR scaled-down replicas preserves much of the original-scale predictive performance while significantly reducing training cost. We further find that learned representations and predictive trajectories remain aligned across scales. These findings suggest that structural similarity may be more important than network size in determining GNN transferability, opening a path toward scale-equivariant graph architectures.
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All authors Robert Jankowski, Pedro Almagro-Blanco, Marián Boguñá, Melanie Weber, M. Ángeles Serrano
Artificial Intelligence · Cross-listed from cs.CV
Private Face Recognition Training Dataset Publication via Identity-Decoupled and Geometry-Preserving Face Distillation
Shuhuan Chen, Xiangyu Zhu, Weisong Zhao et al. (+6 more)
Publishing private face recognition~(FR) training
datasets is
privacy-sensitive because faces expose identity information. Private FR training
dataset publication mitigates this risk by releasing protected proxies as substitutes for private training faces. However, training FR models with such data introduces an identity paradox: the identity cues that make released faces useful for recognition supervision are also the cues that make them linkable to real individuals. A protected face should be decoupled from the original identity, yet still behave as a reliable identity sample for training. Removing these cues too aggressively may destroy the class structure needed for recognition learning, whereas preserving them too faithfully may increase source-identity linkability. We argue that this paradox stems from conflating source-aligned identity semantics with recognition-useful proxy identity geometry. The former should be suppressed to reduce linkage to private individuals, while the latter should be preserved for FR learning. Based on this insight, we propose Private Face
Distillation, an identity-decoupling and geometry-preserving framework. It uses Orthogonal Geometry Preservation to construct decoupled proxy identities from private identity representations while maintaining hyperspherical geometry, and Relational
Topology Alignment to preserve identity relations for recognition learning. Experiments across multiple domain-shifted FR scenarios show that Private Face
Distillation achieves stronger utility than the evaluated publication baselines. On IJB-C surveillance, it improves $\mathrm{TAR}@\mathrm{FAR}{=}1\text{e-}{3}$ by 3.94% over the baseline while reducing source-identity linkability. These results suggest that private FR training
dataset publication should decouple source-identity correspondence while preserving proxy identity geometry.
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All authors Shuhuan Chen, Xiangyu Zhu, Weisong Zhao, Siran Peng, Tianshuo Zhang, Haoyuan Zhang, Haichao Shi, Xiao-Yu Zhang, Zhen Lei
Artificial Intelligence · Cross-listed from cs.IR
Hierarchical Latent Reasoning for LLM-based Recommendation
Peiyu Hu, Siying Gu, Weihai Lu et al. (+8 more)
Large
Language Models (LLMs) have shown strong potential for recommendation by leveraging their semantic understanding and contextual modeling capabilities. Recent studies further introduce
reasoning mechanisms to improve user preference modeling. However, explicit natural-
language reasoning incurs substantial inference overhead, whereas existing latent
reasoning methods mainly focus on generating or verifying intermediate states, leaving their layer-wise preference roles and contributions insufficiently characterized. We propose HiLaR, a Hierarchical Latent
Reasoning framework with layer-aware reinforcement optimization for LLM-based recommendation. HiLaR constructs temporal-guided hierarchical user preference representations, aligns them with multiple LLM latent
reasoning states, and organizes the
reasoning process from broad preferences to fine-grained current intents. To further optimize the
reasoning trajectory, HiLaR combines final recommendation feedback with layer-aware process rewards derived from the marginal target-likelihood gain of each state. Experiments on four Amazon
benchmark datasets show that HiLaR generally outperforms strong sequential, generative, and LLM-based recommendation baselines. Ablation and sensitivity analyses further verify the contribution of hierarchical representation learning, latent
alignment, and process-level optimization. Our code is available in https://github.com/hupeiyu21/HiLaR.
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All authors Peiyu Hu, Siying Gu, Weihai Lu, Zhuodong Liu, Yuntian Tang, Jiahao Liang, Yiying Xie, Jiang Rong, Zhaokai Luo, Zhiyong Wang, Jia Wang
Artificial Intelligence · Cross-listed from cs.IR
A Structured Knowledge Infrastructure for Domain-Specific Data Asset Discovery
Mengdi Chen, Yuanxin Huang, Yulin Jiang et al. (+1 more)
Enterprise data analytics
agents face two structural failures: generic RAG retrieves the wrong asset (Hit@10=19.1%) and delivers no usage knowledge to prevent metric misinterpretation—stemming from four root causes (C1–C4) ranging from semantic gap and entity ambiguity to schema drift and asset-usage gap. We present a two-layer solution deployed in the commercial advertising data warehouse at Xiaohongshu (5,300+ Hive tables, 14 domains). A three-tier dual-purpose knowledge base (179 documents, eight-section annotation template) serves both
retrieval and generation, with a closed-loop refresh pipeline maintaining day-level freshness (one yes/no approval, 30s hot-reload). The Graph-Guided Retriever (GGR) uses a 2,859-node knowledge graph as a candidate gate with intent routing to deliver 71.6x
token reduction. The Scene-Aware Ranker (SAR) applies 19-class entity recognition and explicit scenario annotations; negative knowledge alone contributes 25 percentage points of Hit@10 gain. On two 100-question
benchmarks, Hit@10 rises from 19.1% to 96.6% (+77.5pp) and knowledge coverage from 56% to 77%, at 4.84–5.33s end-to-end latency.
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All authors Mengdi Chen, Yuanxin Huang, Yulin Jiang, Wei Sun