paperlist.ai

AI conference papers

AI conference paper library

Search papers from 30+ top conferences, including NeurIPS, ICLR, ICML, CVPR, ACL and AAAI. Filter by keyword, then open any paper for its abstract, authors and links.

NeurIPS 2026

Systematic Hazard Sampling: Minimal-Variance Inference for Discrete Diffusion and Flow Models

Seunghwan Jang, Wonje Jeung, SooJean Han

Uniform-noise discrete diffusion and flow models generate sequences non-autoregressively through iterative, context-dependent token replacements. However, these models are typically formulated as time-inhomogeneous CTMC/DTMC processes, sampled using independent Bernoulli change decisions per discretization step. This induces Poisson-binomial variance in per-position jump counts that grows with the number of required edits, leading to the common under-editing (residual noise) and over-editing (cascading substitutions) failure modes that degrade sample quality, especially under tight discretization budgets. We identify this sampler-induced variance as an orthogonal source of degradation, distinct from model-side errors and addressable purely at inference time. We propose Systematic Hazard Sampling (SHS), a training-free, drop-in, and hyperparameter-free inference principle for any sampler that admits a stay-vs.-replace decomposition. SHS models per-token edits as events driven by cumulative hazard (CTMC) or jump mass (DTMC) and triggers an edit whenever this quantity exceeds unit-spaced thresholds with a single random phase per position. For any fixed cumulative mass, this preserves the expected jump count while achieving the minimum conditional variance possible among unbiased integer estimators (at most \(1/4\)), without altering per-jump destination sampling. Experiments on four uniform-noise discrete diffusion and flow language models spanning $\sim$110M to $\sim$3B parameters show that SHS consistently improves sample quality across NFE budgets, with the gains growing with NFE as predicted by the variance gap.

NeurIPS 2026

Synthon Contrastive Learning for Synthesizable 3D Molecule Generation

Nahyun Kim, Seul Lee, Sung Ju Hwang

To consider synthesizability in structure-based drug design (SBDD), a recent approach has proposed to co-design synthetic pathways with molecular conformers of ligands. However, the intermediate conformers are predicted based solely on the current intermediate molecule, without information about the synthons that will be added later along the synthetic pathway. As a result, the intermediate conformers do not adequately reflect the upcoming substructures, creating the geometric mismatch that leads to cumulative errors along the synthetic trajectory. To this end, we propose SYnthon Contrastive LEarning (SYCLE), a synthesizable SBDD framework that injects future synthon information into intermediate conformer prediction via contrastive learning. SYCLE further utilizes this information in the pathway generation policy to guide synthon selection. SYCLE achieves state-of-the-art binding affinity across all 15 LIT-PCBA targets. On CrossDocked2020, SYCLE attains -9.80 kcal/mol and an AiZynthFinder success rate of 68.8\%, nearly double that of the best baseline.

NeurIPS 2026

Synthetic Pre-Pre-Training Improves Language Model Robustness to Noisy Pre-Training Data

Xu Guo, Runyu Peng, Jian Tong, Yunhua Zhou, Haijun Lv, Zhihui Lu, Qipeng Guo

Large language models (LLMs) rely on web-scale corpora for pre-training. The noise inherent in these datasets tends to obscure meaningful patterns and ultimately degrade model performance. Data curation mitigates but cannot eliminate such noise, so pre-training corpora remain noisy in practice. We therefore study whether a lightweight pre-pre-training (PPT) stage based on synthetic data with learnable temporal structure helps resist noisy data during the pre-training (PT) stage. % Concretely, we draw PPT sequences from many randomly initialized RNNs and keep the PT recipe unchanged. Across various corruption settings, our method consistently improves robustness to noise during PT, with larger relative gains at higher noise levels. % The gains generalize across corruption types and naturally noisy web corpora. For a 1B-parameter model, a synthetic PPT stage with only 65M tokens achieves the same final loss as the baseline while using up to 49% fewer natural-text PT tokens across different noise levels. Mechanistic analyses suggest PPT does not immediately suppress attention to noisy tokens. Rather, PPT-initialized models gradually downweight attention between corrupted tokens during noisy PT. This indicates that synthetic PPT inhibits noise self-modeling and shapes the subsequent optimization trajectory.

NeurIPS 2026

Synthetic Anchor-Assisted Prototype Alignment for Heterogeneous Federated Learning

Zhihao Hao, Bob Zhang

Heterogeneous Federated Learning (HFL) aims to enable collaboration among clients with diverse model architectures and non-IID data distributions, where direct parameter aggregation is often infeasible and prototype-based aggregation may suffer from semantic drift. Existing methods usually construct global references from client-side representations, making the alignment process vulnerable to local statistical bias and noisy or corrupted prototype updates. In this paper, we propose SATPL, a synthetic anchor-assisted prototype alignment framework that introduces externally generated synthetic anchors as auxiliary semantic priors for HFL. Instead of treating Large Language Models (LLMs) as infallible semantic oracles, SATPL uses LLM-generated class descriptions and generative models to construct reproducible reference prototypes for supervised tasks with known class semantics. Each client maps its heterogeneous local representation into a shared anchor space through a lightweight projection head, while an alignment-weighted aggregation rule assigns lower weights to prototypes that are highly inconsistent with the corresponding anchors. The blockchain component is used as an auditable recording layer for anchor commitments and aggregation metadata, rather than as the source of the statistical robustness guarantee. Experiments under architecture heterogeneity, label skew, and random prototype corruption show that SATPL improves accuracy, convergence stability, and robustness over representative HFL baselines. Additional analyses evaluate anchor quality, temperature sensitivity, and system overhead, clarifying both the benefits and limitations of synthetic-anchor-based alignment.

NeurIPS 2026

SynthForensics: Benchmarking and Evaluating People-Centric Synthetic Video Deepfakes

Roberto Leotta, Salvatore A Sambataro, Claudio V Ragaglia, Mirko Casu, Yuri Petralia, Francesco Guarnera, Luca Guarnera, Sebastiano Battiato

Modern T2V/I2V generators synthesize people increasingly hard to distinguish from authentic footage, while current evaluation suites lag: legacy benchmarks target manipulation-based forgeries, and recent synthetic-video benchmarks prioritize scale over realistic human depiction. We introduce SynthForensics, a people-centric benchmark of $20{,}445$ videos from 8 T2V and 7 I2V open-source generators, paired-source from FF++/DFD reals, two-stage human-validated, in four compression versions with full metadata. In our paired-comparison human study, raters prefer SynthForensics in $71$--$77\%$ of head-to-head comparisons against each of nine existing synthetic-video benchmarks, while facial-quality metrics fall within the FF++/DFD baseline range. Across 15 detectors and three protocols, face-based methods drop $13$--$55$ AUC points (mean $27$) from FF++ to SynthForensics and a further $23$ under aggressive compression; fine-tuning closes the gap at a backward cost on legacy benchmarks; re-training shows synthetic and manipulation features largely disjoint for most detectors. We release dataset, pipeline, and code.

NeurIPS 2026

Synsema: Syntax-Guided Learning of Semantically Valid Programs

Levin Winter, Cong Li, Hao Sun, Zhendong Su

Generating inputs for systems that process highly structured data, such as compilers, is difficult due to the dual of syntactic and semantic constraints. Traditional approaches rely on manually crafted rules that require deep domain expertise and are difficult to maintain, while modern machine learning methods have to learn syntax and semantics simultaneously which is challenging. We present Synsema, a novel approach that combines insights from both worlds by reformulating the generation of semantically valid programs as a syntax-guided reinforcement learning task. By guaranteeing syntactic validity through the grammar, the agent needs to only learn the language's semantic rules, sampling from a drastically reduced space. We implement our approach in the context of Java and Rust which both exhibit strict semantic requirements, and train an agent to generate interesting and well-formed programs using fine-grained semantic feedback. Applied to compiler testing, we systematically compare different syntax-guided generation procedures with an unconstrained baseline and show that by restricting the sampling space, Synsema learns semantic rules efficiently. While an average of only 0.03% of programs generated by the baseline are semantically valid, Synsema produces a diverse set of programs with a 7.29% success rate that trigger 2.6x more compiler behaviors. In addition, Synsema discovered 5 previously unknown bugs in production compilers, validating the practical impact of our approach.

NeurIPS 2026

SyncLight: Single-Edit Multi-View Relighting

David Serrano-Lozano*, Anand Bhattad, Luis Herranz, Jean-Francois Lalonde, Javier Vazquez-Corral

We present SyncLight, a method to enable consistent, parametric control over light sources across multiple uncalibrated views of a static scene conditioning in a single view. While single-view relighting has advanced significantly, existing generative approaches struggle to maintain the rigorous lighting consistency essential for multi-camera broadcasts, stereoscopic cinema, and virtual production. SyncLight addresses this by enabling precise control over light intensity and color across a multi-view capture of a scene, conditioned on a single reference edit. Our method leverages a multi-view diffusion transformer trained using a latent bridge matching formulation, achieving high-fidelity relighting of the entire image set in a single inference step. To facilitate training, we introduce a large-scale hybrid dataset comprising diverse synthetic environments-curated from existing sources and newly designed scenes-alongside high-fidelity, real-world multi-view captures under calibrated illumination. Surprisingly, though trained only on image pairs, SyncLight generalizes zero-shot to an arbitrary number of viewpoints, effectively propagating lighting changes across all views, without requiring camera pose information. SyncLight enables practical relighting workflows for multi-view capture systems. Dataset, code, and models will be released upon acceptance.

NeurIPS 2026

SynTeX-FL: Cross-Modal Text Transfer in Federated Learning for Medical Visual Question Answering

Min Hyuk Kim, Do Hyun Ki, Seokbong Yoo

Medical visual question answering (VQA) and federated learning (FL) have gained prominence as critical methods for clinical artificial intelligence, with VQA supporting image-based diagnostic reasoning and FL allowing institutions to train models jointly without sharing sensitive patient data. However, existing approaches face substantial challenges in cross-modal vertical FL settings, where each participating institution possesses unimodal, modality-specific textual data without access to paired multimodal samples. To address this limitation, we introduce SynTeX-FL, a federated framework designed to enable cross-modal text transfer for medical VQA without sharing raw data. Moreover, SynTeX-FL applies a reconstruction-based text synthesis model that extracts clinically relevant semantics from medical images to generate cross-modal textual representations. Furthermore, the proposed method incorporates modality-specialized low-rank adaptation (LoRA) modules to enhance modality-aware representations for each imaging domain. During aggregation, the central server employs discriminator-based quality scores and source-aware weighting strategies to integrate heterogeneous client contributions. Extensive experiments on multimodal medical VQA benchmarks demonstrate that SynTeX-FL significantly outperforms current FL baselines, providing a robust and efficient solution for cross-modal reasoning in decentralized clinical environments.

NeurIPS 2026

SynMQG: Disentanglement and Mutual-Information Optimization for Synergistic Multi-modal Question Generation

Junjie Zhang, ShengYong Ding, Shuangyin Li

Synergistic multi-modal question generation aims to generate questions grounded in synergistic semantics distributed across multiple modalities, rather than being specified by isolated information from any single modality alone. In this setting, modality-specific cues and cross-modal shared semantics must be jointly well-organized so that the generated question reflects synergistic multi-modal contribution and captures genuinely synergistic information. However, this task faces two core challenges: *how to disentangle multi-modal information for synergistic information modeling*, and *how to preserve synergistic contribution during question generation*. To address these challenges, we propose **SynMQG**, a synergistic dependency-aware framework derived from a mutual-information-based theoretical analysis, which integrates disentangled representation learning with synergy-aware optimization for synergistic multi-modal question generation. Specifically, SynMQG disentangles the multi-modal context into visual-specific, textual-specific, and shared semantics, thereby explicitly organizing heterogeneous information for synergistic information modeling. Based on these disentangled representations, it further introduces a reasoning chain as an intermediate scaffold to structure cross-modal information before generation. Moreover, SynMQG proposes GRPO with a mutual-information-based synergy reward, which explicitly measures whether the generated question preserves effective contribution of synergistic information, encouraging the model to generate questions that depend on synergistic multi-modal semantics rather than superficial single-modal cues. Experiments on ScienceQA and MultimodalQA show that SynMQG consistently outperforms representative MQG baselines across standard text-generation metrics, MLLM-based evaluation, and human evaluation, while generating questions with stronger synergistic multi-modal grounding and higher multi-modal dependency. The source code is available at https://anonymous.4open.science/r/MQG-8E57.

NeurIPS 2026

SynIB: Informational Bottleneck for Maximizing Synergy in Multimodal Learning

Konstantinos Kontras, Teodora Popordanoska, Thomas Strypsteen, Christos Chatzichristos, Matthew Blaschko, Maarten De Vos, Paul Liang

A central objective in multimodal learning is to capture synergy: task-relevant information that arises only from the joint use of multiple modalities, and is not available from any single modality alone. While most approaches operate at the architectural level through larger or more complex fusion models, we propose a complementary axis: shaping the training objective itself. Standard training often emphasizes unimodal or redundant information, falling short on examples that require cross-modal reasoning. We formalize multimodal synergy through information theory and introduce the Synergistic Information Bottleneck (SynIB), a scalable objective that targets synergy directly. To prioritize learning synergy, SynIB motivates the model to predict accurately from all modalities while penalizing confidence when information from any modality is withheld. Alongside the standard task loss, the model runs forward passes with one modality masked at a time and is penalized for remaining confident, which would indicate reliance on unimodal cues rather than cross-modal interactions. We validate SynIB in two regimes. On synthetic XOR tasks where the ground-truth synergy is known by construction, standard training fails to recover it while SynIB does. On five real-world benchmarks, including three MultiBench affective tasks, Hateful Memes with CLIP-ViT and DeBERTa backbones, and a controllable irony extension of CREMA-D we introduce, SynIB improves accuracy on synergy-dependent examples by up to 7.8\% and overall accuracy by up to 3.8\%.

NeurIPS 2026

SynGeo: Synergizing Seeing and Proving through Revisable Geometric States

Tianyi Xu, Liu Yang, Wenjun Gao, Junyu Ou, Zhe Zhao, HaiBin Wen, Maolin Wang, Ye Wei

Geometry problem solving is a canonical testbed for machine intelligence, requiring systems to interpret diagrams, ground symbolic constraints, and perform rigorous deduction. Yet current approaches expose a persistent gap between seeing and proving: multimodal large language models can flexibly inspect diagrams but may hallucinate unsupported relations, while symbolic solvers provide checkable derivations but are brittle to incomplete or misgrounded formalization. We introduce SynGeo, a state-centric framework for synergizing seeing and proving in geometry problem solving by making the geometric representation revisable during inference. SynGeo first constructs a predicate state from diagram--text evidence and tests it with symbolic reasoning. When proof search fails or stagnates, symbolic diagnostics guide image-grounded revisiting, repairing solver-incompatible predicates before another deductive attempt. When the symbolic route remains unresolved, a complementary MLLM branch provides an image-grounded reasoning path from the problem evidence. On Geometry3K and PGPS9K, SynGeo achieves state-of-the-art performance across both Choice and Completion settings. With GPT-4o as the backbone, it reaches \(90.2\%\) accuracy in both settings on Geometry3K, and 90.1% Choice accuracy and 88.6% Completion accuracy on PGPS9K. Ablations show that both feedback-guided revisiting and complementary reasoning are necessary, supporting a broader view of geometry problem solving as an adaptive loop between what a system sees, what it formalizes, and what it proves.

NeurIPS 2026

SynGS: Synergistic Continual Learning and Change Detection with Gaussian Splatting

Rui Huang, Haojie Tao, Sen Gao, Qing Guo

3D Gaussian Splatting enables high-fidelity, real-time novel view synthesis, yet adapting it to dynamic, evolving scenes remains challenging. Existing methods rely on fixed static-dynamic partitioning, which suffers from persistent partitioning errors and cannot adapt during optimization. To tackle this issue, we introduce SynGS, a unified 3D Gaussian framework for continual scene updating and multi-view change detection. We cast static-dynamic decomposition as a learnable task, jointly optimizing per-Gaussian change probabilities and scene representations for iterative refinement. We further propose a change-guided update strategy that modulates illumination-aware photometric corrections via learned probabilities, separating transient appearance variations from authentic scene changes. Projecting Gaussian-level probabilities to the image plane yields 3D-consistent change masks, enabling mutual optimization between reconstruction and change localization. An illumination-decoupled refinement step further improves mask quality. Evaluations on standard benchmarks show that SynGS outperforms baseline methods on scene update and change detection tasks, while fully retaining the real-time rendering capability of 3D Gaussian Splatting. Code will be released upon acceptance.

NeurIPS 2026

SynDORBench: Evaluating LVLM Perceptual Robustness Under Physically Constrained Visibility Conditions

Jeremy S G Yee, Daniel Zhengkui Wang, Zhiyuan Zhang, Avinash Anand, Timothy Liu, Benedict Chan, Aik B Ng, Simon See

Large vision-language models (LVLMs) have demonstrated remarkable performance on multimodal reasoning benchmarks, yet their perceptual reliability under physically constrained imaging conditions remains poorly understood. Existing evaluations predominantly assume ideal visual inputs and therefore fail to characterize how camera distance, illumination, viewpoint, and pixel density fundamentally affect semantic recoverability. We introduce SynDORBench, the first physically grounded benchmark for evaluating LVLM perceptual robustness under DORI-calibrated conditions aligned with human visual capability standards. SynDORBench comprises over 54k question--answer pairs generated through a controllable synthetic pipeline that systematically varies viewing distance, lighting, camera geometry, and action pose according to physically interpretable pixel-density regimes. To support scalable low-visibility supervision, we further propose a discernibility annotation framework that propagates human perceptual labels using mask-conditioned statistical features and ensemble learning. We evaluate 16 open-source LVLMs, a commercial LVLM baseline, and YOLO11x across human-presence classification and action recognition tasks under progressively degraded visibility conditions. Our results reveal that perceptual failure in LVLMs is strongly governed by pixel density and physical imaging constraints rather than model scale alone. Surprisingly, several compact open-source LVLMs outperform larger commercial baselines and substantially exceed YOLO11x robustness under long-range and low-light conditions. SynDORBench establishes a new benchmark paradigm for physically grounded multimodal evaluation, enabling systematic analysis of LVLM reliability under real-world perceptual constraints and direct comparison against human visibility thresholds.

NeurIPS 2026

SynBench: A Benchmark for Differentially Private Text Generation

Yidan Sun, Viktor Schlegel, Srinivasan Nandakumar, Iqra Zahid, Yuping Wu, Yulong Wu, Hao Li, Jie Zhang, Warren Del-Pinto, Goran Nenadic, Siew-Kei Lam, Anil A Bharath

Synthetic text generation with Differential Privacy (DP) guarantees emerges as a principled approach that can enable the sharing of sensitive datasets across institutional and regulatory boundaries, while bounding the risks of re-identification and membership inference. LLM-based methods deliver promising results; however, comparisons are exacerbated by differing evaluation setups and "private" datasets, potential pre-training contamination is not considered and guarantees are not verified with DP audits. To advance this field, we introduce a unified evaluation framework with standardised utility and fidelity metrics and privacy audits, encompassing nine curated datasets that capture domain-specific complexities such as technical jargon, long-context dependencies, and specialised document structures. In a large-scale empirical study, we benchmark LLM-based state-of-the-art DP text generators of varying sizes (between 1--8B). Our results indicate that DP synthetic text generation remains an unsolved challenge, with quality deteriorating more as the private datasets deviate further from the generators' pre-training corpora. Our novel synthetic text membership inference attack (MIA) explains this observation: Synthetic data quality is overestimated when LLMs have been pre-trained---without DP---on portions of the "private" data to be generated. Finally, our work provides the first quantitative evidence that this "public pre-training and private generation" paradigm invalidates the guaranteed privacy bounds of real-world private datasets.

NeurIPS 2026

Symplectic Reck: In-Situ Learning of Gaussian Quantum Operations

Janet Zhong, Renwen Yu, Charles Roques-Carmes, Paul-Alexis MOR, Aviv Karnieli, David A B Miller, Shanhui Fan

Programmable photonic circuits are a natural platform for physical learning, where the learned computation is performed by the hardware itself rather than by a digital model. We propose a physical learning algorithm and architecture for continuous-variable quantum systems where a photonic circuit is configured in situ to invert an unknown lossless Gaussian quantum operation. Our contributions are threefold. (i) Theory: we prove that generic $S \in \mathrm{Sp}(2 N, \mathbb{R})$ admits a triangular decomposition into two-mode and single-mode symplectic gates. (ii) Architecture: we introduce the symplectic Reck mesh, a triangular array of two-mode symplectic gates that physically realizes this factorization, generalizing the unitary Reck mesh widely used in optical neural networks. (iii) Physical learning: when placed after an unknown Gaussian unitary, the mesh is trained in situ by nulling local response blocks one at a time using simple optical probes and measurements. We identify a measure that predicts when training becomes hard and numerically demonstrate finite-shot training and robustness to gate errors. Our work extends self-configuring optical networks from unitary to symplectic transformations, bringing physical learning to continuous-variable quantum photonic hardware.

NeurIPS 2026

Symplectic Parallel Scan: A Neural Hamiltonian Framework for Accelerated Scientific Simulation

Sungwoo Park

Neural Hamiltonian models provide a principled approach to learning dynamical systems by embedding the symplectic structure of physical evolution into the model class. However, this geometric prior alone has not made Hamiltonian neural networks efficient long-horizon simulators: an M-step trajectory is still typically generated by M sequential updates, and common training objectives based on force matching or one-step prediction provide only local supervision. We introduce Symplectic Parallel Scan (SPS), a neural Hamiltonian framework that algebraizes the learned dynamics through a finite-dimensional Poisson algebra of Hamiltonian generators. SPS constructs a Neural--Poisson Composition DAG closed under projected products and Poisson brackets, whose elements induce symplectic Hamiltonian drivers organized as a Lie-group walk. The associativity of this flow composition enables a parallel prefix-scan over Hamiltonian drivers, reducing the sequential depth of trajectory propagation from $\mathcal O(M)$ to $\mathcal O(\log M)$ while keeping each prefix inside the symplectic flow family. The resulting model is trained through a global trajectory-matching objective rather than only local force labels. Across quantum spin and molecular dynamics benchmarks, SPS achieves substantial wall-clock acceleration over sequential HNN baselines, improves long-horizon prediction accuracy, and reduces energy drift and symplectic violation under extrapolation.

NeurIPS 2026

Symplectic Neural Operators for Learning Infinite Dimensional Hamiltonian Systems

Makara Yeang, Yusuke Tanaka, Takashi Matsubara, Takaharu Yaguchi

The modeling and simulation of infinite-dimensional Hamiltonian systems are central problems in mathematical physics and engineering, but they pose significant computational and structural challenges for standard data-driven architectures. In this work, we introduce the Symplectic Neural Operator, a neural operator architecture designed to preserve the symplectic structure intrinsic to Hamiltonian PDEs. We provide a theoretical characterization of their symplecticity and establish a rigorous long-term stability result based on the combination of symplectic structure preservation and learning accuracy. Numerical experiments on canonical Hamiltonian PDEs corroborate this theoretical result and show that SNOs exhibit improved energy behavior compared with non-structure-preserving neural operators.

NeurIPS 2026

Symmetry Guarantees Statistic Recovery in Variational Inference

Daniel Marks, Dario Paccagnan, Mark van der Wilk

Variational inference (VI) is a central tool in modern machine learning, used to approximate an intractable target density by optimising over a tractable family of distributions. As the variational family cannot typically represent the target exactly, guarantees on the quality of the resulting approximation are crucial for understanding which of its properties VI can faithfully capture. Recent work has identified instances in which symmetries of the target and the variational family enable the recovery of certain statistics, even under model misspecification. However, these guarantees are inherently problem-specific and offer little insight into the fundamental mechanism by which symmetry forces statistic recovery. In this paper, we overcome this limitation by developing a general theory of symmetry- induced statistic recovery in variational inference. First, we characterise when variational minimisers inherit the symmetries of the target and establish conditions under which these pin down identifiable statistics. Second, we unify existing results by showing that previously known statistic recovery guarantees in location–scale families arise as special cases of our theory. Third, we apply our framework to distributions on the sphere to obtain novel guarantees for directional statistics in von Mises–Fisher families. Together, these results provide a modular blueprint for deriving new recovery guarantees for VI in a broad range of symmetry settings.

NeurIPS 2026

Symb-xMIL: Symbolic Explanations for Multiple Instance Learning in Digital Pathology

Yanqing Luo, Julius Hense, Niklas Prenißl, Andreas Mock, Klaus Robert Muller, Thomas Schnake, Mina Jamshidi Idaji

Explanations of multiple instance learning (MIL) models are widely used for validation and discovery in digital histopathology. Existing methods primarily rely on heatmaps that highlight influential regions but do not explain how evidence from different tissue regions is combined to produce a prediction. This limits interpretability, especially when decisions depend on interactions between tissue features. We introduce Symbolic explainable MIL (Symb-xMIL), a post-hoc explanation framework that quantifies how a MIL model’s behavior aligns with human-readable decision rules, expressed as logical relationships (e.g., AND, OR, NOT) between input features. These alignment scores reveal semantic patterns underlying the model’s predictions. We evaluate Symb-xMIL on synthetic and real-world histopathology datasets. On synthetic MIL data, Symb-xMIL reliably recovers ground-truth logical rules. In a clinical tumor detection task, the best-aligned rules uncover heterogeneous decision patterns and expose hidden model errors. On an HPV-prediction task on TCGA-HNSCC, a cohort of head and neck cancer, our framework refines patient survival stratification beyond HPV status with potential clinical relevance. Overall, Symb-xMIL extends MIL explainability beyond visual attribution toward structured, rule-based reasoning, enabling more transparent and semantically grounded interpretation of model predictions.

NeurIPS 2026

SwitchLingua V2: Agent-Driven Code-Switching via Digital Clones

Peng Xie, Yequan Bie, Jianda Mao, Hao Chen, Yangqiu Song, Kani Chen

Code-switching (CS) involves shifting between two or more languages during a conversation or utterance, a phenomenon often driven by social context and the identity of the speaker. The scarcity of authentic CS data remains a critical bottleneck in developing robust Automatic Speech Recognition (ASR) systems for bilingual communities. While Large Language Models (LLMs) have been employed to synthesize CS data, direct generation approaches typically produce utterances that are pragmatically unnatural and fail to capture the nuanced stylistic variations of real-world bilingual speakers. In this paper, we propose an archetype-driven code-switching dialogue generation framework via agent-based digital cloning. Moving beyond static prompting, our method facilitates autonomous conversations between multi-agent digital clones imbued with specific bilingual archetypes and established linguistic theories. The generation pipeline integrates constrained persona sampling, real-world topic injection, turn-by-turn accommodation dynamics, and multi-dimensional language checking. Applying this framework, we synthesize highly idiomatic bilingual dialogues across 14 diverse language pairs with 260K textual dialogues (1.04M turns of text samples) and convert them into over 1,558 hours of high-fidelity audio data, yielding the SwitchLingua V2 dataset. To rigorously validate our approach and the generated data, we employ a comprehensive evaluation protocol including both advanced LLM assessments and human bilingual expert evaluations. Extensive experiments demonstrate that SwitchLingua V2 produces CS dialogues that are structurally diverse, pragmatically motivated, and socially situated, holding the potential to offer a highly scalable and reliable pathway for advancing multilingual ASR research.

Keep browsing

More papers from top AI conferences

30+ top conferences, by conference, year, and topic.