Econometrics
Conditionally linear, matrix normal state space models
Drew D. Creal, Marcelo C. Medeiros and Rodrigo Sarlo
We develop a class of linear state space models for
matrix-valued time series data where the state is a latent
matrix normal process. We derive
matrix versions of the Kalman filter, log-likelihood, and smoother enabling estimation of the latent state
matrix as well as the model's parameters. To conduct
Bayesian inference, we provide
algorithms that draw from the joint posterior distribution of the latent state
matrices conditional on the observed data and parameters. We apply these methods to a large panel of U.S. macroeconomic time series across the 50 U.S. states. The proposed framework accommodates mixed-frequency data, heteroskedasticity, and outliers within a unified
matrix-valued structure. Empirically, we find that a small number of latent factors captures the joint dynamics across states and variables, providing a parsimonious and scalable approach to modeling high-dimensional macroeconomic systems.
Read the full abstract ▾
Statistical Mechanics
Exact Nonlinear Active Microrheology in Diffusive Single-File Systems
Aurélien Grabsch and Olivier Bénichou
Active microrheology probes a crowded medium by forcing a tracer and measuring its response. In single-file transport, where particles cannot overtake, a constant force $F$ produces a subballistic displacement $\langle X_t\rangle\simeq \sqrt{t}\,\xi(F)$ together with a persistent bath deformation. Despite decades of work, the exact nonlinear response at arbitrary force has remained confined to a few special solvable models. Here we remove this restriction by combining recent advances in hydrodynamic transport coefficients, a pressure-balance formulation of the local drive, and single-file duality, which eliminates the resulting moving boundary. This yields a closed exact boundary-value problem for general diffusive single files, determining both $\xi(F)$ and the full density profile. For overdamped Brownian particles with general interactions, all microscopic interaction details enter only through the
equilibrium equation of state, bringing realistic interacting systems, including finite-width quasi-one-dimensional channels, within exact reach. The solution also reveals universal global laws; in particular, the bath-density dipole is fixed by the applied force independently of the interaction potential.
Read the full abstract ▾
Robotics
PASSAGE: Scaling Scene-Aligned Motion Learning for Perceptive Humanoid Traversal in Cluttered Environments
Yuxuan Ma, Zicheng Zeng, Chunlin Peng et al. (+17 more)
Humanoid robots can step over, squeeze past, and duck under obstacles, but learning to select and coordinate these behaviors from onboard perception remains challenging. Many existing approaches rely on task-specific reinforcement-learning objectives or curated motion libraries, making broad behavioral coverage costly. We present PASSAGE, a perception-conditioned planner–tracker framework for
humanoid traversal. Using virtual reality and inertial motion capture, we collect 100 h of scene-aligned human motion across 1,500 cluttered scenes. A conditional flow-matching planner generates short-horizon references from motion history, a local destination, and a
robot-centric multi-layer elevation map, while a perceptive whole-body tracker executes them at 50 Hz with
geometric feedback. Real-time chunking promotes inter-chunk consistency, and planner-side RL post-training under the frozen tracker further improves closed-loop performance. Without skill annotations or obstacle-specific policies, one planner–tracker pair selects and composes traversal behaviors across unseen geometries. In simulation, component ablations quantify the contribution of each stage. Across three independent training seeds, scaling captured data from 6 to 100 h increases mean contact-free success from 48.1% to 68.9% on held-out scenes, while the final model with validated scene augmentation reaches 70.3%. The fully onboard system integrates
egocentric 3D
LiDAR perception, online occupancy mapping, 6.25 Hz planning, and 50 Hz control on a Jetson AGX Orin; tests across 50 unseen physical layouts demonstrate traversal without prebuilt maps or offboard computation.
Read the full abstract ▾
All authors Yuxuan Ma, Zicheng Zeng, Chunlin Peng, Zhoujian Li, Zetong Zhao, Zhikai Zhang, Yunrui Lian, Han Xue, Sikai Liang, Weiyi Zhu, Mulin Chen, Chenghuai Lin, Jiayu Zeng, Yanwei An, Songan Zhang, Jiayuan Gu, Jilong Wang, Jingbo Wang, He Wang, Li Yi
Artificial Intelligence
Which LLM is Best for Translating Natural Language Goals to PDDL
Tomas Balyo, Lukas Chrpa and G. Michael Youngblood
Bridging the gap between human intent and machine execution remains a challenge in automated planning, where expressing goals in formal
languages like PDDL restricts accessibility to non-experts. This paper empirically evaluates whether current Large
Language Models (LLMs) can reliably translate natural
language testing goals, written in informal
language by
video game testers, into well-formed PDDL targets suitable for classical planning. We present a carefully designed
prompt template, integrating insights from iterative experimentation, aimed at maximizing both accuracy and response coherence from multiple state-of-the-art LLMs. Six contemporary models are systematically assessed on correctness, speed, and error tendencies using real-world, domain-specific
benchmarks. All models demonstrate high correctness, exceeding 92%, with Gemini 2.5 Flash achieving the highest accuracy at 96% and the lowest incidence of false positives, while GPT-4.1 leads in response speed. Despite these advances, critical distinctions exist in model performance, and occasional failures arise from
language ambiguity and limitations in domain representation. Our analysis underscores both the significant progress and ongoing gaps in enabling LLMs to act as robust bridges between natural
language objectives and automated planning pipelines.
Read the full abstract ▾
Logic
A minimal type of Morley rank $\omega$ in a partial differential field
Piotr Kowalski
We consider the Kolchin-generic complete $1$-type of the inviscid Burgers equation $Dx=x\partial x$ in a differentially closed field of characteristic zero with two commuting derivations. We prove that this type has Lascar rank $1$ and Morley rank $\omega$.
Computers and Society
"If I Had to Buy Just ONE: Galaxy S26 Ultra": Auditing AI-Generated Product Recommendations
Lucas G. Uberti-Bona Marin, Thales Bertaglia, Giovanni Astante et al. (+5 more)
Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this raises difficult questions about the bias and impartiality of such advice. In response, we conduct an AI audit of popular chatbots using real commercial-advice queries. First, we curate a
dataset of 2,528 real commercial-advice queries (ConsumerQ). Then, we evaluate 1,536 responses to product queries from popular AI chatbots: ChatGPT (chatbot and API), Google Gemini (chatbot and API), and Google Search (AI Overviews). We find that ChatGPT expresses a first-person product preference in 79% of product-recommending responses, compared with 7% for Gemini and 2% for AI Overviews, while the products recommended often change across repeated requests. Displayed sources vary strongly: for the same query, the ChatGPT and Gemini interfaces share only 5.4% of domains on average, with no domain in common in 76.7% of comparisons. APIs provide a different view from their corresponding interfaces, with mean domain overlaps of 12.0% for ChatGPT and 14.8% for Gemini, and also differ in the types and layers of source information they expose. Our findings show that neither isolated responses nor API observations can be assumed to represent the commercial advice consumers encounter. Independent audits of AI-mediated commercial advice should therefore account for repeated responses, consumer-facing conditions, and the source layer being observed.
Read the full abstract ▾
All authors Lucas G. Uberti-Bona Marin, Thales Bertaglia, Giovanni Astante, Bram Rijsbosch, Gijs van Dijck, Anikó Hannák, Gerasimos Spanakis, Konrad Kollnig
Optimization and Control
Near-Optimal Exact-Value Zeroth-Order Complexity for Smooth Strongly Convex Optimization
Wendao Wu, Haihan Zhang, Chenheng Zhang et al. (+5 more)
We study deterministic adaptive optimization of globally $\beta$-smooth, $\mu$-strongly convex functions using exact scalar function values. Queries and outputs lie in $B_2^d(R)$, and the minimizer lies in $B_2^d(R/2)$. Set $\kappa=\beta/\mu$, $Q=\beta R^2/\epsilon$, and $D_d=(d/\log(ed))^{1/3}$. For sufficiently large $d$ and $0<\epsilon\le c_\epsilon\beta R^2$, the minimax value complexity $N_\epsilon$ satisfies \[ \begin{aligned} N_\epsilon &\ge c d\min\{\sqrt Q,\sqrt\kappa,D_d\},\\ N_\epsilon &\le C d\min\left\{ \sqrt Q,\sqrt\kappa[1+\log_+(Q/\kappa)] \right\}, \end{aligned} \] where $c,C,c_\epsilon>0$ are universal constants and $\log_+(t)=\max\{0,\log t\}$. The lower bound uses an exactly shielded smooth chain and batched delayed rotations; the upper bound combines finite differences, acceleration, and restart. When $\min\{Q,\kappa\}\le D_d^2$, these bounds match up to constants in the accuracy-dominated regime $Q\le\kappa$ and at constant relative accuracy $\epsilon=\Theta(\mu R^2)$. For arbitrarily higher accuracy in the range $\kappa\le D_d^2$, the bounds differ by at most $1+\log(\mu R^2/\epsilon)$; the optimal accuracy dependence remains unresolved in general.
Read the full abstract ▾
All authors Wendao Wu, Haihan Zhang, Chenheng Zhang, Yanyi Li, Chunyuan Zheng, Cong Fang, Haoxuan Li, Zhouchen Lin
Information Theory
Counterexample to a Proposed Capacity Characterization of the Relay Channel
Chun Hei Michael Shiu
We present a counterexample to the relay channel capacity characterization proposed in a recent work, which is based on properties of typical sequences. The counterexample uses a binary relay channel with a noiseless source-destination component and binary symmetric relay links. An achievable scheme exceeds the proposed capacity characterization, thereby disproving the characterization of the capacity for general relay channels. We highlight that the counterexample is obtained with the assistance of ChatGPT-6 Astra and the proofs are simplified with human effort.
Read the full abstract ▾
Human-Computer Interaction
Calmables: Demonstrating Closed-Loop Infrared Earables for Thermal Biofeedback and Relaxation Support
Valeria Zitz, Michael Küttner, Jonas Hummel et al. (+3 more)
We present Calmables, a walk-up demo of a closed-loop infrared earable that uses smart-ring heart rate to create subtle, ear-localized warming cues. Building on work on thermal comfort and in-ear infrared stimulation, Calmables explores warmth at the ear as a biosignal-adaptive cue for brief recovery moments following acute activation. A smartphone first establishes an individual resting baseline and derives a personalized heart-rate threshold, while the earable controller independently enforces an over-temperature cut-off and communication fail-safes. During the guided demo flow, attendees complete a brief rapid-breathing activation phase until their heart rate reaches the personalized threshold. This triggers an ear-localized warming cue followed by a short relaxation phase, while physiological changes are displayed on a live dashboard. Attendees can also manually explore different infrared stimulation intensities. To contextualize the demo, we report preliminary placebo-controlled UX ratings from 18 participants: participants rated the active prototype higher on perceived relaxation and perceived recovery support than an identical-looking placebo. Together, the demo illustrates how infrared earables can make physiological feedback tangible through subtle, biosignal-adaptive thermal cues.
Read the full abstract ▾
All authors Valeria Zitz, Michael Küttner, Jonas Hummel, Taku Inoue, Tobias Rödiger, Michael Beigl
Accelerator Physics
Coherent collective amplification of terahertz microbunching seeded by laser frequency beating in relativistic electron beams
Wencai Cheng, Yin Kang, Kaiqing Zhang et al. (+5 more)
High-power, continuously tunable terahertz sources based on free-electron
lasers require precise control of electron-beam microstructures. Quantitative prediction of the modulation amplitude across a broad frequency range remains challenging because the
laser-induced distribution and its collective
evolution must be treated together. To describe this coupled
evolution, a nonlinear model is developed for microbunching seeded by a frequency-beating
laser heater. The non-Gaussian heater-exit distribution is obtained by optical phase averaging and propagated through multistage compression in six-dimensional phase space. Source-induced correlations are retained, with space charge, coherent synchrotron radiation, and
radio-frequency wakefields evaluated self-consistently. The wavelength-dependent bunching response is thereby connected to the
laser beat frequency,
laser power, and compression partition. The short-wavelength double peak is found to be governed mainly by longitudinal space charge, while bunching near a selected wavelength can be enhanced by redistributing compression at fixed total compression. The predicted source modulation and downstream response are benchmarked against Elegant and IMPACT-Z, respectively. Experimental measurements of the wavelength-dependent bunching factor at different
laser pulse energies are found to agree well with the trends predicted by theory and simulation. The resulting framework provides a computationally efficient and predictive route to optimizing electron-beam microbunching for tunable, high-power terahertz generation at free-electron-
laser facilities.
Read the full abstract ▾
All authors Wencai Cheng, Yin Kang, Kaiqing Zhang, Zhen Wang, Duan Gu, Guangling Chen, Chao Feng, Haixiao Deng
Game Theory
Second-Best Gains from Trade in Matching Markets
Xiaohui Bei, Bo Li, Wenhao Wu et al. (+1 more)
We study gains from trade (GFT) in two-sided matching
markets with independent private types and arbitrary downward-closed feasibility constraints. The second-best
benchmark is the maximum expected GFT achievable by a
Bayesian incentive-compatible, interim individually rational mechanism that is strongly budget balanced at every report profile. These constraints generally preclude attaining the first-best GFT and raise the question of how much efficiency must be lost. We prove that the second-best GFT is at least one half of the first-best GFT in every such matching
market. This recovers and generalizes the recent $1/2$ guarantee for bilateral trade by Liu, Qin, Ren, and Wang to
markets with multiple buyers and sellers and arbitrary downward-closed feasibility constraints. Together with their matching upper bound for bilateral trade, our result establishes a tight worst-case ratio of $1/2$ for this general class of matching
markets. Our proof builds on the virtual-GFT framework of Brüstle, Cai, Wu, and Zhao to reduce the problem to a one-parameter Lagrangian. The main step is a
geometric, edge-by-edge analysis based on first-best edge-selection regions, combined with a randomized contraction in rank space. The same analysis also determines the exact worst-case efficiency for every level of outside subsidy. In particular, even a small outside subsidy can significantly improve the worst-case guarantee.
Read the full abstract ▾
All authors Xiaohui Bei, Bo Li, Wenhao Wu, Shengwei Zhou
Artificial Intelligence
Beyond Truncation: Rethinking LLM Decoding as Ensemble Pruning
Dunyao Xue, Chengshuo Du, Zhengbo Wang et al. (+2 more)
We introduce Mahalanobis-Ensemble Decoding (ME-Decoding), a novel Large
Language Model (LLM) decoding framework that frames candidate
token selection as ensemble
pruning. Existing selection strategies rely predominantly on scalar probabilities, ignoring
geometric semantic relationships and causing candidate redundancy. Meanwhile, current geometry-aware methods often require complex optimization or directly reweighting the original
token probabilities, leading to significant computational overhead or inference instability. To address this, we formulate decoding as a subset optimization problem using a Mahalanobis distance-driven objective to enhance semantic diversity while preserving high probabilities. Specifically, we dynamically discount redundant generation paths using a
token similarity
matrix, constructed via an adaptive-bandwidth kernel over
token embeddings. We further devise an efficient greedy selection
algorithm with near-linear complexity in the candidate size under early stopping, while establishing its theoretical approximation guarantees. This renders ME-Decoding a robust, plug-and-play module with negligible inference overhead. Extensive experiments across diverse
reasoning and generation tasks demonstrate that our method consistently achieves strong performance.
Read the full abstract ▾
All authors Dunyao Xue, Chengshuo Du, Zhengbo Wang, Wenlin Dai, Cheng Meng
Computational Complexity
A Structural Proof of the Lower Bound 21 for $3\times3$ Matrix Multiplication over $\mathbb F_2$
Shuxing Yang, Rui Zhao, Junyao Wu et al. (+13 more)
We prove that the tensor rank of $3\times3$
matrix multiplication over $\mathbb F_2$ is at least $21$. The structural proof, independently developed by Qiushi Engine, converts occupation constraints on a single tensor factor into algebraic relations coupling all three factors. Certified quotient-rank bounds and finite geometry force any hypothetical $20$-term decomposition to have first-factor
matrix-rank profile $(16,1,3)$. The ranks of the corresponding split-flattened summands therefore sum to $27$, exactly the rank of the full split flattening. Equality in rank subadditivity forces their images to form a direct sum; normalization by the inverse flattening then makes the summands pairwise annihilating idempotents. An explicit product identity for
matrix multiplication implies that at most one first factor can be invertible, contradicting the three forced by the profile. The same obstruction constrains $22$-term decompositions attaining the split-rank bound. The complete proof, including the finite quotient bounds, is formalized in Lean. The accompanying research trajectory records Qiushi Engine's long-horizon autonomous research, from numerical experiments and quotient constructions to the structural proof.
Read the full abstract ▾
All authors Shuxing Yang, Rui Zhao, Junyao Wu, Yize Wang, Wenhao Li, Fujia Chen, Taowen Deng, Shenzhan Hong, Yaqi Li, Zichen Li, Jincheng Mi, Yuang Pan, Kaihao Zhu, Junjie Yang, Hongsheng Chen, Yihao Yang
Quantum Physics
Thermal entanglement on a frustrated tetrahedron: Probing quantum resources through concurrence and stabilizer structure
Reza Pourkhodabakhshi, Francis Dominie, Deep Gajera et al. (+1 more)
In a frustrated
spin system, highly entangled eigenstates can form a separable thermal mixture. We study this distinction for four
spin-$1/2$ moments on a tetrahedron, the elementary unit of the pyrochlore
lattice, with the general four-parameter exchange
Hamiltonian. Tetrahedral
symmetry allows a restricted search over thermal-state decompositions to be carried out by linear optimization, yielding an upper bound on multipartite concurrence and explicit separable decompositions where this bound vanishes. At low temperature, the concurrence maps show extended separable regions near the all-in–all-out limit, whereas suppression near the
spin-ice point is narrowly localized. Heating broadens the latter region as nearby multiplets are repopulated. The optimization identifies fully separable thermal states even when the eigenstate-averaged concurrence remains large. Negativity independently confirms finite-temperature
entanglement in selected coupling regions. A mixed-state stabilizer Rényi diagnostic, used to explore
quantum magic, favours different couplings and can increase locally with temperature. These results connect thermal
entanglement to the splitting and population of
symmetry multiplets, and provide a practical basis for identifying frustrated
spin configurations for studies of
quantum resources beyond the ground-state limit.
Read the full abstract ▾
All authors Reza Pourkhodabakhshi, Francis Dominie, Deep Gajera, Stephanie H. Curnoe
Computation and Language
LocQE: Principled Domain Adaptation for Localisation Quality Estimation by Leveraging Post-Edits
Kathy Hämmerl, Gabriel Bretschner and Joern Wuebker
Learned quality estimation (QE) models such as COMETKiwi are widespread and work well for general machine translation evaluation. However, they are known to struggle on unseen domains, limiting their performance in a real-world localisation context. We show that they are insensitive to some important factors in localisation, such as whether numbers are translated accurately, or even whether the correct number of spaces and punctuation are preserved in a translation. Further, a key capability for optimisation of machine translation is the ability of QE models to accurately rank different translations of a single segment, which suffers significantly from the domain transfer. In the absence of large-scale direct assessment data, we propose principled fine-tuning approaches to reduce the domain gap with even small amounts of post-editing data. Using a multi-task fine-tuning approach and a simple tokeniser intervention, we create a QE model which proves markedly better at distinguishing preferred post-edits from rejected initial translations in a localisation context. We show that preferences and artificial continuous scores stabilise each other, and argue that to calibrate metrics both in terms of their absolute scores and comparisons between translation of the same source, both types of signal are needed.
Read the full abstract ▾
Algebraic Geometry
On curves with one place at infinity
Abdallah Assi and Wael Mahboub
Let K be an algebraically closed field of characteristic zero. Many of the
geometric properties of the plane curves in K^2 with one place at infinity can be obtained from the arithmetic of numerical
semigroups associated with these curves. In this survey, we recall the main properties of these
semigroups as well as the
geometric results obtained from these properties. We also address some problems.
Read the full abstract ▾
Robotics
Calibrated Probabilistic Obstruction Reasoning with Vision-Language Models for Grasping in Clutter
Thanh-Tuan Tran, Ngoc-Chien Chu, Thanh Nguyen Canh et al. (+3 more)
Retrieving a target from clutter requires deciding whether to grasp the target, remove a blocker, or defer. Existing methods typically commit to a single obstruction graph or removal strategy, ignoring uncertainty across alternative scene interpretations. They also rely on miscalibrated
vision-
language model (VLM) predictions and can produce pairwise obstruction relations that are jointly inconsistent. Moreover, current approximations provide no guarantees about the impact of discarded hypotheses on the final decision. We propose CPOR-Grasp, a calibrated probabilistic obstruction-
reasoning framework that propagates uncertainty from pairwise evidence to action decisions. CPOR-Grasp calibrates and fuses VLM, depth, and amodal-mask cues to estimate obstruction probabilities, induces a distribution over valid obstruction graphs, and marginalizes over these graphs to compute the likelihood that the target is accessible or that a given blocker should be removed. To make inference tractable, it retains only the highest-probability graphs and derives a total-variation bound on the discarded probability mass, enabling certified decisions, adaptive stopping, and principled deferral. On synthetic and real UNOBench scenes, CPOR-Grasp outperforms state-of-the-art baselines. Calibration error decreases from 0.1416 to 0.0185 on the Gemini Robotics backbone, while graph truncation matches exact inference on 99.74% of decisions using 56 times fewer graphs. In real-world experiments, CPOR-Grasp achieves a 77.8% average success rate, surpassing SOTA baselines.
Read the full abstract ▾
All authors Thanh-Tuan Tran, Ngoc-Chien Chu, Thanh Nguyen Canh, Nak Young Chong, Nguyen-Viet Ha, Xiem HoangVan
Differential Geometry
Spectral properties for critical metrics of the volume functional
Rafael Diógenes, Jaciane Gonçalves and Ernani Ribeiro Jr
In this article, we investigate spectral properties of compact $V$-static
manifolds, namely, compact
manifolds with boundary whose metrics are critical points of the volume functional under a scalar
curvature constraint. We derive sharp estimates for the first Steklov
eigenvalue and the entire fourth-order Steklov spectrum. We further obtain a Lichnerowicz-type lower bound for the first
eigenvalue of the drifted
Laplacian naturally associated with the $V$-static potential. In the corresponding equality cases, we obtain
rigidity results characterizing the Euclidean ball and the hemisphere.
Read the full abstract ▾
Computer Vision
Mask IPL: Noise-Free Intrinsic Position Learning via Computation Graph Clipping for Event-Based Spike-Driven Tracking
Yimeng Shan and Malu Zhang
Spiking Neural Networks (SNNs) match the event-driven nature of event cameras and naturally extract spatiotemporal features. These properties have motivated a series of recent studies on event-based tracking with SNNs. Intrinsic Position Learning (IPL) acquires strong position information without introducing additional parameters, making it a mainstream approach for position encoding in event-based spike-driven tracking. However, the mechanism behind its effectiveness lacks systematic theoretical analysis. Moreover, our analysis reveals that IPL introduces noise in both forward and backward propagation. The former increases inference error, while the latter prevents parameters from converging to better solutions. This paper presents a systematic analysis of IPL and demonstrates that its effectiveness stems from the synergy between IPL and multi-stage convolution. The zero blocks in the joint tensor act as zero padding for convolution, and the resulting boundary effect propagates layer by layer through multi-stage convolution. Every parameter update is therefore driven by a gradient that perceives the relative displacement between template and search frames. Positional encoding added after the convolutional stage cannot provide this information. We further propose a simple Computation Graph Clipping method that applies a validity mask determined by the layout to the operations of every layer, making invalid regions equivalent to zero padding in both forward and backward propagation. This eliminates the noise without introducing additional parameters and makes the actual gradient coincide with the ideal gradient. We name the improved method Mask IPL. Without increasing parameters or computational cost, Mask IPL improves the AUC of the Tiny-scale tracker on FE108, FELT, and VisEvent, and consistently improves the Base-scale tracker as well.
Read the full abstract ▾
Physics and Society
BanglaShop-CRS: A User-Centric Bangla Dataset for Conversational Recommendation
Tabia Tanzin Prama, Christopher M. Danforth and Peter Sheridan Dodds
Conversational recommender systems~(CRS) enable users to express preferences, constraints, and feedback through natural
language interaction. However, existing CRS resources are concentrated in English and other high-resource
languages, leaving Bangla and code-mixed Bangla–English settings underrepresented. To address this gap, we introduce BanglaShop-CRS, a large-scale user-centric synthetic Bangla conversational recommendation
dataset grounded in real e-commerce behavior. It contains 27,178 multi-turn dialogues, 274,802 utterances, and 3.6M
tokens across 10 product domains. Our generation pipeline incorporates user purchase histories, positive and negative feedback, and review texts to maintain consistency between dialogue content and user preferences. We evaluate BanglaShop-CRS under catalog-constrained and open-vocabulary recommendation protocols. Results show that dialogue context improves recommendation quality, while fine-tuning yields further gains across models. Human evaluation by five native Bangla-speaking annotators confirms the fluency, informativeness, logicality, and coherence of the dialogues, with significant agreement across all dimensions. Factual-grounding evaluation further shows stronger
alignment with correct than shuffled user records, with substantial inter-annotator agreement ($\kappa=0.65$) and comparable human and GPT-5.1 judgments. BanglaShop-CRS provides a scalable
benchmark for advancing conversational recommendation in Bangla.
Read the full abstract ▾
Audio and Speech Processing
Absolute Quality Ratings of Speech Enhancement Systems by Listeners of Different Ages and Degrees of Hearing Loss
Matteo Torcoli, Chih-Wei Wu, Andrea Esposito et al. (+6 more)
Speech Enhancement (SE) supports listening, particularly for older adults with age-related hearing loss. Yet, enhanced
Speech Quality (SQ) is commonly evaluated by young normal-hearing listeners, and how their ratings translate to older adults remains under-explored. We compared absolute SQ ratings from 40 younger normal-hearing listeners (20-30 years) and 67 older listeners (60-95 years) with diverse audiometric profiles, after screening. Test materials comprised natural dialogues with realistic backgrounds. SQ differences between SE systems that were clear for younger listeners were smaller or inseparable in older groups, regardless of hearing status. Hearing loss severity was associated with lower absolute ratings, but did not strongly modulate the contraction in separable SQ differences. A small, audiometrically mixed subgroup of older listeners showed younger-like rating patterns, suggesting that peripheral audiology alone cannot explain the contraction.
Read the full abstract ▾
All authors Matteo Torcoli, Chih-Wei Wu, Andrea Esposito, Phillip A. Williams, Katrien Cambier, William Wolcott, Antonio Curci, Nicholas S. Reed, Mark Laureyns
Statistical Methodology
Doubly valid and doubly sharp sensitivity analysis to unobserved confounding for survival outcomes
Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich et al. (+2 more)
Time-to-event outcomes are central in oncology and rare diseases, where treatment effects are often summarized by differences in survival curves or Restricted Mean Survival Time (RMST). In real-world data, estimating these causal effects relies on the absence of unobserved confounding, an assumption that is rarely satisfied. We develop a sensitivity analysis framework for causal treatment effects with survival outcomes under the Marginal Sensitivity Model (MSM). We introduce doubly valid and doubly sharp (DVDS) bounds for differences in survival functions and RMST, extending recent DVDS results to the time-to-event setting while accounting for informative censoring. In practice, our method yields tighter bounds and improved computational efficiency compared to a previous approach from the literature, on simulated and real data. For tractability, we assume independence between censoring and unobserved confounding, a limit that should be addressed in future works.
Read the full abstract ▾
All authors Jean-Baptiste Baitairian, Bernard Sebastien, Rana Jreich, Sandrine Katsahian, Agathe Guilloux
Data Structures and Algorithms
A sampling Lovász Local Lemma
Dimitris Achlioptas
We give an approximately uniform sampler for satisfying assignments of constraint satisfaction problems that satisfy $4\mathrm e p(\Delta+1)^2\le1$, where $p$ is the largest constraint-violation probability under the uniform product distribution, and $\Delta$ is the maximum degree of the dependency graph. The
algorithm invokes the recent efficient approximate counting
algorithm of Liu, Wang, Yin, Zhang, and Zhou~\cite{CountingLLL} as a subroutine and returns a satisfying assignment sampled within total-variation distance $\varepsilon$ of the uniform distribution in $(n+m/\varepsilon)^{O(k\Delta\log D)}$ time, where $n$ and $m$ are the numbers of variables and constraints, $D$ is the common domain size, and $k$ bounds the constraint arity. We also give an asymmetric product-form condition under which both counting and sampling are efficient. To supply the counts needed in the additional regime, we extend the marginal-expansion proof of Liu et al.
Read the full abstract ▾
Analysis of PDEs
Determining Schrodinger Potentials on Riemannian Manifolds
Lu Chen, Yan Jiang, Hongyu Liu et al. (+1 more)
We prove that the Dirichlet-to-Neumann map uniquely determines a smooth real Schrödinger potential on any prescribed compact connected smooth Riemannian
manifold of dimension at least three with nonempty smooth boundary. The proof combines local continuation of Green kernels with uniform estimates for the associated integral representations on shrinking spheres. These estimates yield local recovery of the potential, and a continuation argument extends this recovery throughout the
manifold.
Read the full abstract ▾
All authors Lu Chen, Yan Jiang, Hongyu Liu, Longyue Tao
Physics and Society
Ensuring proportionality: a logical model for compensatory seats added to multi-member constituencies
Frederik Ravn Klausen
How many compensatory seats are needed to ensure (full) proportionality in two-tier electoral systems with multi-member constituencies? This question is answered by estimating the discrepancy between seats and votes rigorously, probabilistically and empirically. In 1919, Pólya showed that the Jefferson/D'Hondt method favours large parties. This seat surplus accumulates across constituencies. In some countries (e.g., Portugal, Poland, Spain and Turkey), this accumulation directly leads to quantifiable disproportionality in the final parliament. Elsewhere compensatory seats counteract this accumulated seat surplus. Using a logic-first approach, closed formulas for the required number of compensatory seats are derived given regional and national apportionment methods, the number of relevant parties $n$ and the size of the largest party $p$. If there are $c$ constituencies, it is derived logically that a two-tier system built on local D'Hondt apportionment in multi-member constituencies needs around $\frac{c}{2}(n-\frac{1}{p})$ compensatory seats to ensure proportionality. In words, that is half a compensatory seat per constituency times a number that increases with the number of parties and the size of the largest party. The validity of the derived formulas is analysed empirically using the extensive recent CLEA
dataset. Many quantities do not depend much on the total parliament sizes, which means that the variance can be reduced by artificially varying the house sizes. Portugal, Poland, Spain and Turkey all employ a one-tier D'Hondt system and the analysis shows that if they were to add compensatory seats to the system, reasonable numbers would be 40, 75, 75 and 120, assuming that no other properties of the systems change.
Read the full abstract ▾
Human-Computer Interaction
"Okay, I've Actually Softened My Take on This": How People in Decentralized Social Media Reason about the Appropriateness of Generative AI
Romina Mahinpei, Manoel Horta Ribeiro, Andrés Monroy-Hernández et al. (+1 more)
Generative AI (GenAI) is increasingly integrated into social media, raising questions about whether, where, and how it belongs. In decentralized social media (DSM), these decisions are distributed across users, developers, moderators, and administrators, making GenAI a collective governance challenge. At the same time, public discourse often flattens arguments to broad pro- or anti-AI positions that offer little insight into what people actually find (in)appropriate and why. Through 20 semi-structured interviews with people from Mastodon and Bluesky, structured around seven GenAI scenarios, we examine how people reason about GenAI's appropriateness in DSM. We find that participants drew conditional boundaries around particular GenAI configurations through distinct, salient, and weighted considerations spanning technology, integration, and use. We conceptualize this as boundary drawing and show how making such boundaries visible can support more grounded design, policy, and collective deliberation around GenAI in DSM.
Read the full abstract ▾
All authors Romina Mahinpei, Manoel Horta Ribeiro, Andrés Monroy-Hernández, Sohyeon Hwang
Machine Learning
Rethinking Critic Learning in PPO: Understanding and Mitigating Value Flattening
Yizhuo Li, Jianhao Yan, Yun Luo et al. (+9 more)
In reinforcement learning for large
language models, Proximal Policy Optimization (PPO) commonly uses a critic to estimate state values and reduce the variance of policy updates. However, we uncover a systematic failure mode in PPO critics, which we call Value Flattening: state values, estimated from multiple Monte Carlo continuations, change sharply across intermediate states while critic predictions remain comparatively flat. We further observe this phenomenon in a controlled FrozenLake environment and find that it becomes more pronounced as the state space grows. Our theoretical and empirical analyses relate Value Flattening to an implicit variance penalty in the critic loss and redundant updates from temporally correlated states with similar gradients. Motivated by these findings, we introduce SParse Proximal Policy Optimization (SP$^3$O), which applies the value loss to only a few well-separated states in each response to mitigate both effects. Experiments on Qwen3-Base show that SP$^3$O with only three states supervised per response can mitigate Value Flattening and consistently improve the learned policy across model sizes and evaluation suites. Together, our results identify Value Flattening as an important yet overlooked failure mode of critic learning in standard PPO and show that a simple sparse supervision strategy can mitigate it.
Read the full abstract ▾
All authors Yizhuo Li, Jianhao Yan, Yun Luo, Zhi Wang, Futing Wang, Rong-Xi Tan, Kanghui Tian, Ganqu Cui, Ning Ding, Peilin Zhao, Yafu Li, Yu Cheng
Data Structures and Algorithms
Total Variation Distance Estimation through Domain Reduction
Arnab Bhattacharyya, Graham Cormode, Yucheng Fu et al. (+1 more)
Computing the total variation (TV) distance between succinctly represented high-dimensional distributions is generally intractable. We give an FPRAS for TV distance between mixtures of product distributions and, more generally, for a natural class of structured probabilistic
circuits. Our main technique is a novel application of domain reduction: Given a family of feature vectors indexed by assignments, we use Lewis-weight sampling to replace the assignment domain by a
polynomial-size weighted subset that simultaneously approximates the sum of absolute values of every linear projection. For mixtures of product distributions, we construct such reduced domains incrementally over the coordinates, obtaining the first FPRAS with running time
polynomial in both the dimension and the number of mixture components. We then extend the approach to smooth, structured-decomposable probabilistic
circuits with a common structured architecture.
Read the full abstract ▾
All authors Arnab Bhattacharyya, Graham Cormode, Yucheng Fu, Kuldeep S. Meel
Cryptography and Security
Echo: Learning-based Matching Decompilation using Trusted Back Translation
Jun Bi, Xiangxin Fang, Aarsh Chaube et al. (+3 more)
Neural decompilers can recover readable and recompilable source code from binaries, but their predictions remain difficult to trust. Matching decompilation addresses this problem by searching for source code whose recompiled assembly exactly matches the target, providing stronger evidence of correctness. However, exact matching remains challenging for optimized binaries under unknown compilation configurations. We present Echo, a matching decompilation system based on trusted back-translation. Our key insight is to use compilation not only for verification, but also as trusted feedback to guide iterative search. Echo first uses a domain-specific model to generate candidate programs and compilation configurations. It recompiles these candidates, measures assembly-level similarity, and synthesizes promising code-configuration pairs. Remaining mismatches are then progressively repaired using rule-based rewriting, neural refinement, and
reasoning-based refinement. We evaluate Echo on function-level
benchmarks and the Mirai malware binary. Compared with the strongest baseline, Echo produces 2.43x more exact matches on average and achieves the highest structural similarity to ground-truth source code. On Mirai, Echo matches 2.75x and 7.4x as many functions as GPT-5.6 and Codex, respectively.
Read the full abstract ▾
All authors Jun Bi, Xiangxin Fang, Aarsh Chaube, José Wesley De Souza Magalhães, Rodrigo C. O. Rocha, Michael O'Boyle
Dynamical Systems
Dynamics of planar integrable Kepler billiards with a focused hyperbolic branch
Daniel Jaud and Lei Zhao
We consider the integrable dynamics of a planar Kepler billiard in the plane bounded by a branch of a hyperbola focused at the Kepler center. As a sequel to our previous work \cite{JZ2}, we use the existence of the foci-caustic circle to identify an
elliptic curve on which the dynamics is linearized and we analyze Cayley's condition on $n$-periodic orbits in this setting. By taking various limits also we discuss the dynamics of planar Kepler billiards with boundary being a focused
parabolic arc, as well as a straight line. In this last case, our approach offers a different way to deduce the
elliptic curve as in \cite{Felder}.
Read the full abstract ▾