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Jing Jiang

Publications and source records attributed to Jing Jiang.

At least 19 recordsLinked to original sources
arXiv2026

Effects of Theory of Mind and Prosocial Beliefs on Steering Human-Aligned Behaviors of LLMs in Ultimatum Games

Large Language Models (LLMs) have shown potential in simulating human behaviors and performing theory-of-mind (ToM) reasoning, crucial for complex social interactions. We investigate ToM reasoning's role in aligning agentic behaviors with human norms in negotiation tasks, using the ultimatum game as our referenced task. We initialized LLM agents with different prosocial beliefs (Greedy, Fair, Selfless) and reasoning methods (chain of thought and ToM reasoning of varying levels), examining their decision-making processes and outcomes across multiple LLMs, including reasoning models like o3-mini and DeepSeek-R1 Distilled Qwen 32B. We perform 2,700 simulations to show that ToM reasoning enhances behavioral alignment with human norms, decision-making consistency, and negotiation outcomes. Consistent with prior findings, reasoning LLMs exhibit limited capability compared to ToM-enhanced LLMs, with different game roles benefiting from different ToM orders. Agents almost always chose strategies consistent with their assigned beliefs, and doing so aligned better with human norms in nearly every role, yet Greedy and Selfless agents still fell short of their norms in outcomes. Human verification further revealed that Llama 3.3 70B produces reasoning most consistent with its actions and beliefs. Our findings advance understanding of ToM's role in human-AI interaction and cooperative decision-making. The code used for our experiments can be found at https://github.com/Stealth-py/UltimatumToM.

cs.CL↗
arXiv2026

Ultrasmall (3,0) carbon nanotube: a superconducting unit with strong electron-phonon coupling

Based on density-functional first-principles calculations, we study the electronic structures, lattice dynamics, and electron-phonon coupling (EPC) properties of the ultrasmall one-dimensional (3,0) carbon nanotube (CNT). Our studies reveal that the (3,0) CNT hosts characteristic collective phonon modes, in which the corresponding atomic vibrations can significantly modulate the electronic self-consistent potential and induce strong EPC. Our results further show that the (3,0) CNT exhibits robust phonon-mediated superconductivity under ambient pressure, with a superconducting transition temperature reaching $\sim$ 33 K ($μ^*$ = 0.10). We also find that although hole doping enhances the density of states (DOS) near the Fermi level, it simultaneously hardens the strongly coupled phonon modes and suppresses the total EPC. This result indicates that phonons play a more crucial role in the superconductivity of ultrasmall CNTs rather than the DOS alone. Therefore, we suggest that the ultrasmall (3,0) CNT can serve as a superconducting unit hosting phonon modes with strong EPC, and propose a strategy for discovering new phonon-mediated superconductors by identifying superconducting units.

cond-mat.supr-con↗
arXiv2026

Gelfand--Kirillov dimensions of highest weight modules for basic classical Lie superalgebras

We give exact Gelfand--Kirillov (GK) dimension formulas for simple highest weight modules of the basic classical Lie superalgebras considered in this paper. For the type II orthosymplectic algebras $D(m,n)$ and $B(m,n)$, we identify the PBW associated variety as a product of factor supports evaluated at highest weights for two compatible Borel subalgebras. This gives algorithms for arbitrary complex highest weights, without integrality or typicality hypotheses. In type $B$, the terminal factor is $\mathfrak{osp}(1|2n)$, for which we derive an insertion formula that distinguishes integer and half-integer shifted coordinates. For $G(3)$, $F(4)$ and $D(2,1;α)$, finite odd-reflection procedures give exact formulas, their GK spectra, and finite-dimensionality criteria; we also describe the dependence on $α$ in the last case. In type I, the GK dimension equals that of the simple even highest weight module for the distinguished Borel. The computations use the established ordinary Lie algebra formulas involving Lusztig's $\mathbf a$-function and Robinson--Schensted insertion.

math.RT↗
arXiv2026

Federated Semantic Residuals for Multimodal Recommendation

Federated Recommendation Systems (FedRSs) learn collaborative scores from interaction histories on device and rank items by them, so a higher score means a higher rank. Item text and images add semantic information that can reorder the list. Existing Federated Multimodal Recommenders (FedMRs) fuse this content into their models, which can obscure how content reorders the list. We therefore propose the SEmantic Residual Adapter (SERA), which keeps content out of an existing FedRS and adds semantic residual scores to its collaborative scores. Content can then lift an item above another only when its semantic residual score exceeds the other's by more than their collaborative score gap. This score grows with the item's content similarity to the user's interaction history and is capped at a residual bound. We prove that for any content, an item can enter the list only if its collaborative score is within twice this bound of the lowest one in the list. A larger bound lets content lift more items, including irrelevant ones, so the bound trades off how far content reaches against accuracy. With a validated bound, SERA improves all three FedRSs on all six datasets without adding personalized parameters. It outperforms FedMRs in most comparisons and lifts some FedRSs above centralized baselines.

cs.IR↗
arXiv2026

Pathwise Alignment for Collaborative Filtering: Bilateral Conditional Flow Regularization

Collaborative filtering predicts user-item interactions from implicit feedback. We focus on matrix factorization (MF) trained with the Bayesian Personalized Ranking loss. This loss updates each user embedding only through that user's observed items and sampled negatives, so sparse feedback gives MF few constraints. To constrain each user through other users, we propose Pathwise Alignment for Collaborative Filtering (PACF), which trains MF with one network shared by all training pairs. At points between user and item embeddings, a flow matching term trains the network to predict the user-to-item step, reversed and scaled for sampled negatives. A trajectory consistency term pulls each user's predicted path to end at the observed item. Both terms update the embeddings, so the network is discarded after training, thereby maintaining the efficiency of MF at inference. We prove that a network with bounded sensitivity to its input point accurately fits users who share an item only if their embeddings are close, so these users constrain one another. Against eleven discriminative and generative baselines, PACF achieves the highest performance on all seven datasets. Its relative gains over MF are largest where feedback is sparsest. The shared network thus supplies the missing constraints through other users.

cs.IR↗
arXiv2026

Electron- and hole-doped superconductivity in two-dimensional electride ZrX$_2$ (X = Cl, Br, I)

Two-dimensional (2D) superconducting systems have recently emerged as a frontier in condensed matter physics, driven by their unique physical properties and potential applications. Using first-principles calculations, we show that monolayer ZrX$_2$ (X = Cl, Br, I), which is a family of 2D semiconducting electrides, hosts phonon-mediated superconductivity under both electron and hole dopings. Electron doping in ZrX$_{2}$ can achieve high superconducting transition temperatures at very low concentrations, with ZrBr$_2$ reaching $T_c = 30.0$ K at 0.01 \textit{e}/f.u., followed by ZrCl$_2$ (22.5 K at 0.05 \textit{e}/f.u.) and ZrI$_2$ (18.1 K at 0.01 \textit{e}/f.u.), for which the high $T_c$ originates from the flat conduction band minimum, with Van Hove singularities nearby enhancing the density of states. In contrast, hole doping requires higher concentrations (0.2--0.25 \textit{h}/f.u.) to activate superconductivity, yielding $T_c \sim 2$ K, and approaches a magnetic instability at elevated doping levels. By comparing with InSe and LaBr$_2$, we identify the spatial location and orbital character of excess electrons as key factors determining the strength of hole-doped superconductivity and the nature of competing orders. These findings establish monolayer ZrX$_2$ as a platform for bipolar superconductivity in a 2D electride and provide insights into flat-band engineering for high-$T_c$ superconductors.

cond-mat.supr-con↗
arXiv2026

From Priors to Projections: Geometry and simplified MIMO demodulation of probabilistic shaping

Probabilistic shaping (PS) is a well-known method to achieve improved performance upon a regular quadrature amplitude modulation (QAM) by taking the target constellation and making the distribution of underlying points non-uniform. It has been extensively studied over the years for the additive white Gaussian noise (AWGN) and Rayleigh fading channels. However, the potential of probabilistic shaping in the multiple-input and multiple-output (MIMO) setting needs further investigations. In this paper, we prove that if shaped symbols follow Maxwell-Boltzmann distribution, the optimal maximum a posteriori (MAP) detection is equivalent to the case of uniform constellation with a simple preprocessing step. Our approach has multiple benefits. It enables to utilize the same processing chain for both shaped and unshaped system, which simplifies the receiver architecture. This technique can be applied for both linear MMSE and nonlinear (near-)MAP demapper types. In addition, the complexity of adaptive methods such as sphere decoding can be reduced.

cs.IT↗
arXiv2026

CoFiE: Coarse-to-Fine Evidence Selection for Efficient Streaming Video Understanding

Streaming video understanding requires Vision Language Models (VLLMs) to process growing video streams and answer user questions under tight latency constraints. Existing methods improve efficiency through token pruning and memory-bank schemes, but mainly reduce visual tokens after visual encoding. Consequently, downstream token pruning alone cannot substantially reduce end-to-end latency because the expensive frame encoding cost has already been incurred. We propose CoFiE, a Coarse-to-Fine Evidence Selection framework that decouples evidence selection into a coarse, query-agnostic filtering stage before the vision encoder and a fine, query-specific refinement stage during LLM prefill. CoFiE introduces Novelty-Guided Frame Filtering to retain visually distinctive candidate frames and Query-Specific Evidence Refinement to select the frames most relevant to the user query. This design removes substantial redundancy before frame encoding while preserving query-specific refinement once semantic information becomes available. Experiments show that CoFiE establishes a new state-of-the-art accuracy-efficiency trade-off across multiple video understanding benchmarks, reaching 78.86% accuracy on StreamingBench and 68.72% on OvO-Bench, with improvements of up to 3.15% over prior methods. Even with up to 80% evidence-frame filtering, CoFiE outperforms strong open-source multimodal models while improving end-to-end inference latency by up to 2.54 times.

cs.CV↗
arXiv2026

From Rollout to Reset: A Graph-Based Harness for Autonomous Long-Horizon Manipulation Evaluation

Robot manipulation policies are improving quickly, and real-robot evaluation remains the standard evidence for that progress. It still relies on a human to reset the scene between rollouts, which consumes operator time and leaves the initial state distribution unspecified, so results reproduce poorly. A recent system, AutoEval, automates both reset and scoring, but only for single-step tasks, because a long-horizon rollout can terminate in combinatorially many configurations that no single learned reset policy covers. We present HALTER, a Harness for Autonomous Long-horizon Task Evaluation and Reset, which restores the scene by planning over a library of learned atomic reset skills, so demonstration cost scales with the size of that library rather than with the number of terminal states. HALTER builds a spatial scene graph online from point clouds and vision foundation models, and an LLM reasons over this graph to score the rollout, plan the reset, and verify that the reset succeeded, without collecting labeled success images for any task. On four long-horizon tasks on a Franka arm, HALTER restores the scene in 76% of episodes, against 52% for AutoEval and 65% for a motion-planning reset, and it estimates the completed-skill fraction correctly in 90% of episodes, against 76%. Its reset-verification verdict is correct in 91% of episodes, compared with 78% for AutoEval. It also cuts the operator time of an evaluation campaign by 72% relative to manual reset. We further measure compositional generalization on three held-out tasks, where HALTER resets 74.7% of episodes against 1.3% for a per-task reset policy, and we ablate the scene representation and the graph update rate.

cs.RO↗
arXiv2026

Distribution-Aware GMD Transceiver Design for Probabilistic Shaping in MIMO

Multiple-input multiple-output (MIMO) transceiver and probabilistic shaping (PS) are key enablers for high spectral efficiency in 6G wireless networks. This work proposes a distribution-aware MIMO transceiver optimized for PS constellation symbols, including a Bayesian geometric mean decomposition (BGMD) precoder and a maximum a posteriori VBLAST (MAP-VBLAST) detector. The BGMD precoder uses PS priors in the derivation and equalizes layer gains to facilitate a single modulation and coding scheme for low-complexity transmissions while preserving channel capacity. MAP-VBLAST leverages these PS priors for optimal MAP detection within a successive interference cancellation (SIC) framework. Furthermore, a new codeword-to-layer mapping scheme, termed layer-contained MIMO (LC-MIMO), is proposed. By containing each codeblock (CB) within a single layer, LC-MIMO enables SIC at the CB level, allowing the receiver to exploit the error-correction capability of channel coding to mitigate error propagation. Numerical results show that the BGMD transceiver with LC-MIMO achieves notable performance gains over state-of-the-art methods.

eess.SP↗
arXiv2026

Influence-Oriented Personalized Federated Learning

Federated learning (FL) is a machine learning paradigm where clients with different behaviors and preferences can learn collaboratively without compromising data privacy. Typical FL methods often rely on fixed weighting for parameter aggregation, thereby neglecting the mutual influence among clients. In practice, clients with similar preferences or backgrounds may provide more useful knowledge to each other, which can be leveraged to improve local performance. However, how to quantify such cross-client influence and how to exploit it for personalized aggregation remain underexplored. To address this gap, we propose an influence-oriented Federated learning framework which quantitatively measures Client-level and Class-level Influence to realize adaptive parameter aggregation for each client (FedC^2I for short). Our core idea is to explicitly model the inter-client influence within an FL system via the well-crafted influence vector and influence matrix. Specifically, FedC^2I incorporate influence vectors to quantify client-level influence, enables clients to selectively acquire knowledge from others, and guides the aggregation of feature representation layers. Meanwhile, the influence matrix captures class-level influence in a more fine-grained manner to achieve personalized classifier aggregation. We evaluate the performance of FedC^2I against existing federated learning methods under non-IID settings, and the results demonstrate the superiority of our method in terms of effectiveness, robustness, and interpretability.

cs.LG↗
arXiv2026

DialToM: A Theory of Mind Benchmark for Forecasting State-Driven Dialogue Trajectories

We introduce DialToM, an annotated Theory of Mind (ToM) benchmark built from naturalistic human-human dialogues using a multiple-choice evaluation framework. Concurrent with recent work showing a gap between explicit mental-state inference and applied ToM in synthetic settings~\cite{gu2024simpletom}, we establish a stricter \emph{State-Driven Diagnostic Probe} in which models must forecast state-consistent dialogue trajectories solely from isolated mental-state profiles without dialogue context. Our evaluation reveals a systematic reasoning asymmetry -- LLMs excel at inferring mental states (Literal ToM) but struggle to leverage them for social forecasting (Functional ToM). Crucially, a domain expert achieves 100\% accuracy on this task, proving its validity and establishing a stark human-AI capability gap. Further, a teacher-student reasoning injection probe shows that Gemini 3 Pro -- which establishes the leading baseline -- possesses robust Functional ToM capabilities for context-free forecasting that are transferable to weaker models. DialToM, its evaluation code, and dataset are publicly available at https://github.com/Stealth-py/DialToM.

cs.CL↗
arXiv2026

Highly organized smectic-like packing in vapor-deposited glasses of a liquid crystal

Glasses of a model smectic liquid crystal-forming molecule, itraconazole, were prepared by vapor deposition onto substrates with temperatures ranging from Tsubstrate = 0.78 Tg to 1.02 Tg, where Tg = 330 K is the glass transition temperature. The films were characterized using x-ray scattering techniques. For Tsubstrate near and below Tg, glasses with layered smectic-like structures can be prepared and the layer spacing can be tuned by 16% through choice of Tsubstrate. Remarkably, glasses prepared with Tsubstrate above Tg exhibit much higher structural organization than a thermally annealed film. These results are explained by a mechanism based upon preferred molecular orientation and enhanced molecular motion at the free surface, indicating that molecular organization in the glass is independent of the anchoring preferred at the substrate. These results suggest new strategies of optimizing molecular packing within active layers of organic electronic and optoelectronic devices.

cond-mat.soft↗
arXiv2026

PPOM: Marginalizing Patch-Grid Phase for CLIP-Based Generalizable Vision-Language Prompt Tuning

Prompt tuning adapts CLIP-based vision-language models with few trainable parameters, yet its predictions remain sensitive to the spatial sampling imposed by a frozen vision transformer. In particular, non-overlapping patch tokenization makes predictions depend on the alignment (phase) between image and the patch lattice. To reduce prediction sensitivity to patch-grid alignment, we introduce Patch-Phase Orbit Marginalization (PPOM), a training-free inference operator that treats phase shift as a nuisance variable. Given a patch stride, PPOM evaluates the identity view and reflection-padded translations, pairs opposite shifts into horizontal, vertical, and diagonal antithetic families, and assigns equal mass to these families and the identity prediction to avoid view-count bias during phase integration. In summary, PPOM provides a deterministic interface between prompt adaptation and patch-grid sensitivity. Across multiple prompt-learning hosts, PPOM improves host performance without re-training.

cs.CV↗
arXiv2026

Content Depth Matters in Short-Video Recommendation: Rethinking the Attention Economy

Driven by the attention economy, short-video Recommender Systems (RSs) are primarily optimized to maximize user engagement by promoting videos that capture attention within seconds. These systems inherently favor shallow-content videos that are effective at attracting immediate attention. However, growing evidence suggests that prolonged exposure to such content may negatively affect users' cognitive engagement and mental well-being, raising concerns about the long-term societal impact of the short-video platform. To tackle this challenge, this paper introduces a new metric, the \textbf{Content Depth Score (CDS)}, to quantify the content depth of short videos. CDS measures the extent to which a video is expected to stimulate higher-order cognitive processes, using a seven-level scale grounded in established theories of cognitive psychology and learning. As an initial step toward this vision, we present \textbf{SCOPE-Bench}, the first benchmark for content-depth evaluation in short-video recommendation. Built upon a large-scale open-source short-video dataset, SCOPE-Bench provides CDS annotations for 150K videos, enabling systematic evaluation of RSs from a cognitive-content perspective. Leveraging SCOPE-Bench, we evaluate 13 representative RSs and reveal a consistent preference for shallow-content videos. Moreover, we find that these algorithms recommending cognitively deep content are only marginally better than random selection, highlighting a previously overlooked limitation of existing recommendation objectives. Our code and datasets are available at https://liweidengdavid.github.io/SCOPE-Bench/.

cs.AI↗
arXiv2026

MIThinker: A Plug-and-Play Policy-Optimized Thinker For Motivational Interviewing Counseling

Reasoning large language models (LLMs) have recently made much progress in complex problem-solving, leveraging internal reasoning (or thought) to guide their solution generation. However, existing LLM-based counseling agents, including those using Motivational Interviewing (MI), generate responses without explicitly aligning thoughts with counseling techniques, limiting their effectiveness. We propose MIThinker, a lightweight thinking model that generates therapeutic thoughts to guide MI counseling agents in strategy selection and response generation. To overcome the lack of annotated thought data, we introduce AugR1-MI, an automated pipeline that reverse-engineers counselor's thoughts from observed responses. Through two-stage training combining supervised fine-tuning and reinforcement learning, MIThinker demonstrates improved theory-of-mind assessment and strategy alignment. Comprehensive evaluations show that MindfulMI, our agent leveraging MIThinker, achieves MI competency comparable to state-of-the-art systems with an order of magnitude less computation.

cs.CL↗
arXiv2026

Personalized Additive Modeling for Multi-level Federated Learning

Contemporary AI faces the challenge of balancing generality with user-specific personalization. In federated learning (FL), this challenge is amplified by highly heterogeneous client data with complex non-IID patterns beyond standard IID assumptions. Many existing FL methods are designed for relatively restricted heterogeneity settings (e.g., a fixed number of clusters or a fixed form of personalization), limiting their robustness under complex structures. In this work, we study FL from a \emph{multi-level non-IID} perspective, where client similarity is captured by multiple granularities of shared knowledge: global, subgroup, and client-specific components. This view captures coarse-to-fine relationships while requiring less prior knowledge of task boundaries. Building on this insight, we propose \emph{Federated Multi-level Additive Modeling} (FeMAM), which learns multiple levels of shareable models and constructs personalized predictors via additive composition across levels. To move beyond a fixed structure, FeMAM allows models to grow and be pruned dynamically during training, adapting to diverse federated scenarios. Despite employing multiple models, FeMAM remains cost-friendly by unlocking only a small subset (one level) of models for training at a time. Extensive experiments show that FeMAM effectively approximates diverse complex non-IID structures and consistently outperforms representative clustered and personalized FL baselines.

cs.LG↗
arXiv2026

Selective stabilization of antiferromagnetic orders in FeTe films via local strain engineering

The parent compound FeTe hosts a complex magnetic landscape that is highly susceptible to lattice distortions. Although theoretical models have predicted a bicollinear to dimer antiferromagnetic (AFM) phase transition under tensile strain, its experimental realization and deterministic control has remained elusive owing to severe magnetic frustration. Here, combining high-resolution scanning tunneling microscopy (STM) and density functional theory (DFT) calculations, we demonstrate the selective stabilization of bicollinear and dimer AFM orders in few-layer FeTe films via local uniaxial strain engineering. By mapping the strain fields near dislocation areas in FeTe films and FeTe/FeSe heterostructures, we establish a direct correspondence between specific strain components and the resulting magnetic ground states. We find that uniaxial compression along the Fe-Fe next-nearest-neighbor direction stabilizes the bicollinear AFM order, with the stripe orientation aligning parallel to the compression axis. Crucially, we report the experimental realization of the long-range dimer AFM order, which emerges under anisotropic strain along the Fe-Fe nearest-neighbor direction. This phase manifests as a distinct $\sqrt{2} \times \sqrt{2}$ electronic reconstruction and shares a common Neel temperature with the bicollinear phase. Our findings reveal that anisotropic strain effectively lifts the magnetic degeneracy among competing states. This work provides a robust strategy for the manipulation of elusive magnetic orders and offers insights into the interplay between lattice, spin, and electronic degrees of freedom in iron-based superconductors.

cond-mat.str-el↗