GenAI4ELab/papercli-papers huggingface dataset:
DeepTRACE: Auditing Deep Research AI Systems for Tracking Reliability Across Citations and Evidence
Authors: Pranav Narayanan Venkit; Philippe Laban; Yilun Zhou; Kung-Hsiang Huang; Yixin Mao; Chien-Sheng Wu
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Generative search engines and deep research LLM agents promise trustworthy, source-grounded synthesis, yet users regularly encounter overconfidence, weak sourcing, and confusing citation practices. We introduce _DeepTRACE_, a novel sociotechnically grounded audit framework that turns prior community-identified failure cases into eight measurable dimensions spanning answer text, sources, and citations. DeepTRACE uses statement-level analysis (decomposition, confidence scoring) and builds citation and factual-support matrices to audit how systems reason with and attribute evidence end-to-end. Using automated extraction pipelines for popular public models (e.g., GPT-4.5/5, You.com, Perplexity, Copilot/Bing, Gemini) and an LLM-judge with validated agreement to human raters, we evaluate both web-search engines and deep-research configurations. Our findings show that generative search engines and deep research agents frequently produce one-sided, highly confident responses on debate queries and include large fractions of statements unsupported by their own listed sources. Deep-research configurations reduce overconfidence and can attain high citation thoroughness, but they remain highly one-sided on debate queries and still exhibit large fractions of unsupported statements, with citation accuracy ranging from 40–80\% across systems. Unlike prior factuality and citation metrics that focus on claim correctness or academic summarization, DeepTRACE audits end-to-end GSE/DR behavior, including citation necessity, unsupported-statement rates, and URL-level citation structure.
Convex Efficient Coding
Authors: Will Dorrell; Peter E. Latham; James C. R. Whittington
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Why do neurons encode information the way they do? Normative answers to this question model neural activity as the solution to an optimisation problem; for example, the celebrated efficient coding hypothesis frames neural activity as the optimal encoding of information under efficiency constraints. Successful normative theories have varied dramatically in complexity, from simple linear models (Atick & Redlich, 1990), to complex deep neural networks (Lindsay, 2021). What complex models gain in flexibility, they lose in tractability and often understandability. Here, we split the difference by constructing a set of tractable but flexible normative representational theories. Instead of optimising the neural activities directly, following (Sengupta et al. 2018), we instead optimise the representational similarity, a matrix formed from the dot products of each pair of neural responses. Using this, we show that a large family of interesting optimisation problems are convex. This includes problems corresponding to linear and some non-linear neural networks, and problems from the literature not previously recognised as convex such as modified versions of semi-nonnegative matrix factorisation or nonnegative sparse coding. We put these findings to work in two ways. First, we extend previous results on modularity and mixed selectivity in neural activity; in so doing we provide the first necessary and sufficient identifiability result for a form of semi-nonnegative matrix factorisations. Second, we seek to understand the meaningfulness of single neural tuning curves as compared to neural representations. In particular we derive an identifiability result stating that, for an optimal representational similarity matrix, if neural tunings are `different enough' then they are uniquely linked to the optimal representational similarity, partially justifying the use of single neuron tuning analysis in neuroscience. In sum, we identify an interesting space of convex problems, and use that to derive neural coding results.
Pruning Long Chain-of-Thought of Large Reasoning Models via Small-Scale Preference Optimization
Authors: Bin Hong; Jiayu Liu; Kai Zhang; Jianwen Sun; Mengdi Zhang; Zhenya Huang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Recent advances in Large Reasoning Models (LRMs) have demonstrated strong performance on complex tasks through long Chain-of-Thought (CoT) reasoning. However, their lengthy outputs increase computational costs and may lead to overthinking, raising challenges in balancing reasoning effectiveness and efficiency. Current solutions often compromise reasoning quality or require extensive resources. In this paper, we investigate how to reduce the generation length of LRMs with limited tuning. We analyze generation path distributions and filter generated trajectories through difficulty estimation. Subsequently, we analyze the convergence characteristics of various preference optimization objectives under a unified Bradley-Terry loss based framework. Based on the analysis, we propose Length Controlled Preference Optimization (LCPO) that directly balances the implicit reward related to NLL loss. LCPO can effectively learn length preference with limited data and training. Extensive experiments demonstrate that our method significantly reduces the average output length of LRMs by over 50\% across multiple benchmarks while maintaining the reasoning performance. Our work highlights the potential for computationally efficient approaches in guiding LRMs toward efficient reasoning.
INSTANT: Compressing Gradients and Activations for Resource-Efficient Training
Authors: Tuan-Kiet Doan; Trung-Hieu Tran; Enzo Tartaglione; Nikola Simidjievski; Van-Tam Nguyen
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Deep learning has advanced at an unprecedented pace. This progress has led to a significant increase in its complexity. However, despite extensive research on accelerating inference, training deep models directly within a resource-constrained budget remains a considerable challenge due to its high computational and memory requirements. In this paper, we introduce INSTANT (compressIng gradieNtS and acTivAtions for resource-efficieNt Training), a method designed to address both the computational and the memory bottlenecks when training. INSTANT reduces resource demands during backpropagation by projecting gradients and activations into a low-rank subspace and performing computation within that compressed representation. Experimental results demonstrate that INSTANT achieves a $15\times$ reduction in computational cost and $32\times$ reduction in activation memory with negligible impact on model performance. The code will be made publicly available upon the paper's acceptance.
Graph Random Features for Scalable Gaussian Processes
Authors: Matthew Zhang; Jihao Andreas Lin; Krzysztof Marcin Choromanski; Adrian Weller; Richard E. Turner; Isaac Reid
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: We study the application of graph random features (GRFs) – a recently-introduced stochastic estimator of graph node kernels – to scalable Gaussian processes on discrete input spaces. We prove that (under mild assumptions) Bayesian inference with GRFs enjoys $\mathcal{O}(N^{3/2})$ time complexity with respect to the number of nodes $N$, with probabilistic accuracy guarantees. In contrast, exact kernels generally incur $\mathcal{O}(N^{3})$. Wall-clock speedups and memory savings unlock Bayesian optimisation with over 1M graph nodes on a single computer chip, whilst preserving competitive performance.
Beyond Noisy-TVs: Noise-Robust Exploration Via Learning Progress Monitoring
Authors: Zhibo Hou; Zhiyu An; Wan Du
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: When there exists an unlearnable source of randomness (noisy-TV) in the environment, a naively intrinsic reward driven exploring agent gets stuck at that source of randomness and fails at exploration.
Intrinsic reward based on uncertainty estimation or distribution similarity, while eventually escapes noisy-TVs as time unfolds, suffers from poor sample efficiency and high computational cost. 
Inspired by recent findings from neuroscience that humans monitor their improvements during exploration, we propose a novel method for intrinsically-motivated exploration, named Learning Progress Monitoring (LPM).
During exploration, LPM rewards model improvements instead of prediction error or novelty, effectively rewards the agent for observing learnable transitions rather than the unlearnable transitions.
We introduce a dual-network design that uses an error model to predict the expected prediction error of the dynamics model in its previous iteration, and use the difference between the model errors of the current iteration and previous iteration to guide exploration.
We theoretically show that the intrinsic reward of LPM is zero-equivariant and a monotone indicator of Information Gain (IG), and that the error model is necessary to achieve monotonicity correspondence with IG.
We empirically compared LPM against state-of-the-art baselines in noisy environments based on MNIST, 3D maze with 160x120 RGB inputs, and Atari.
Results show that LPM's intrinsic reward converges faster, explores more states in the maze experiment, and achieves higher extrinsic reward in Atari.
This conceptually simple approach marks a shift-of-paradigm of noise-robust exploration.
Activation Function Design Sustains Plasticity in Continual Learning
Authors: Lute Lillo; Nick Cheney
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In independent, identically distributed (i.i.d.) training regimes, activation functions have been benchmarked extensively, and their differences often shrink once model size and optimization are tuned. In continual learning, however, the picture is different: beyond catastrophic forgetting, models can progressively lose the ability to adapt—loss of plasticity—and the role of the non-linearity in this failure mode remains underexplored. We show that activation choice is a primary, architecture-agnostic lever for mitigating plasticity loss. Building on a property-level analysis of negative-branch shape and saturation behavior, we introduce two drop-in nonlinearities—Smooth-Leaky and Randomized Smooth-Leaky—and evaluate them in two complementary settings: (i) supervised class-incremental benchmarks and (ii) reinforcement learning with non-stationary MuJoCo environments designed to induce controlled distribution and dynamics shifts. We also provide a simple stress protocol and diagnostics that link the shape of the activation to the adaptation under change. The takeaway is straightforward: thoughtful activation design offers a lightweight, domain-general way to sustain plasticity in continual learning without extra capacity or task-specific tuning.
Distilled Pretraining: A modern lens of Data, In-Context Learning and Test-Time Scaling
Authors: Sachin Goyal; David Lopez-Paz; Kartik Ahuja
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: In the past year, distillation has seen a renewed prominence in large language model (LLM) pretraining,
exemplified by the Llama-3.2 and Gemma model families. While distillation has historically been
shown to improve statistical modeling, its effects on new paradigms key to modern LLMs—such as
test-time scaling and in-context learning—remain underexplored. In this work, we make three main
contributions. First, we show that pretraining with distillation yields models that exhibit remarkably
better test-time scaling. Second, we observe that this benefit comes with a trade-off: distillation
impairs in-context learning capabilities, particularly the one modeled via induction heads. Third, to
demystify these findings, we study distilled pretraining in a sandbox of a bigram model, which helps
us isolate the common principal factor behind our observations. Finally, using these insights, we shed
light on various design choices for pretraining that should help practitioners going forward.
Temporally Detailed Hypergraph Neural ODE for Disease Progression Modeling
Authors: Tingsong Xiao; Yao An Lee; Zelin Xu; Yupu Zhang; Zibo Liu; Yu Huang; Jiang Bian; Jingchuan Guo; Zhe Jiang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Disease progression modeling aims to characterize and predict how a patient's disease complications worsen over time based on longitudinal electronic health records (EHRs). For diseases such as type 2 diabetes, accurate progression modeling can enhance patient sub-phenotyping and inform effective and timely interventions. However, the problem is challenging due to the need to learn continuous-time progression dynamics from irregularly sampled clinical events amid patient heterogeneity (e.g., different progression rates and pathways). 
Existing mechanistic and data-driven methods either lack adaptability to learn from real-world data or fail to capture complex continuous-time dynamics on progression trajectories. To address these limitations, we propose Temporally Detailed Hypergraph Neural Ordinary Differential Equation (TD-HNODE), which represents disease progression on clinically recognized trajectories as a temporally detailed hypergraph and learns the continuous-time progression dynamics via a neural ODE framework. TD-HNODE contains a learnable TD-Hypergraph Laplacian that captures the interdependency of disease complication markers within both intra- and inter-progression trajectories. Experiments on two real-world clinical datasets demonstrate that TD-HNODE outperforms multiple baselines in modeling the progression of type 2 diabetes and related cardiovascular diseases.
Should We Still Pretrain Encoders with Masked Language Modeling?
Authors: Hippolyte Gisserot-Boukhlef; Nicolas Boizard; Manuel Faysse; Duarte Miguel Alves; Emmanuel Malherbe; Andre Martins; CELINE HUDELOT; Pierre Colombo
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Learning high-quality text representations is fundamental to a wide range of NLP tasks. While encoder pretraining has traditionally relied on Masked Language Modeling (MLM), recent evidence suggests that decoder models pretrained with Causal Language Modeling (CLM) can be effectively repurposed as encoders, often surpassing traditional encoders on text representation benchmarks. However, it remains unclear whether these gains reflect an inherent advantage of the CLM approach or arise from confounding factors such as model and data scale. In this paper, we address this question through a series of large-scale, carefully controlled pretraining ablations, training a total of 38 models ranging from 210 million to 1 billion parameters, and conducting over 15,000 fine-tuning and evaluation runs. We find that while training with MLM generally yields better performance across text representation tasks, CLM-trained models are more data-efficient and demonstrate improved fine-tuning stability. Building on these findings, we experimentally show that a biphasic training strategy that sequentially applies CLM and then MLM, achieves optimal performance under a fixed computational training budget. Moreover, we demonstrate that this strategy becomes more appealing  when initializing from readily available pretrained CLM models, reducing the computational burden needed to train best-in-class encoder models. We release all project artifacts at \url{https://hf.co/MLMvsCLM} to foster further research.
Multiplayer Nash Preference Optimization
Authors: Fang Wu; Xu Huang; Weihao Xuan; Zhiwei Zhang; Yijia Xiao; Guancheng Wan; Xiaomin Li; Bing Hu; Peng Xia; Jure Leskovec; Yejin Choi
Venue: ICLR 2026 (ICLR 2026 Oral)

Abstract: Reinforcement learning from human feedback (RLHF) has emerged as the standard paradigm for aligning large language models with human preferences. However, reward-based methods grounded in the Bradley–Terry assumption struggle to capture the nontransitivity and heterogeneity of real-world preferences. To address this, recent studies have reframed alignment as a two-player Nash game, giving rise to Nash learning from human feedback (NLHF). While this perspective has inspired algorithms such as INPO, ONPO, and EGPO that offer strong theoretical and empirical guarantees, they remain fundamentally restricted to two-player interactions, introducing a single-opponent bias that fails to capture the full complexity of realistic preference structures. 
This work introduces Multiplayer Nash Preference Optimization (MNPO), a novel framework that generalizes NLHF to the multiplayer regime. It formulates alignment as an $n$-player game, where each policy competes against a population of opponents while being regularized toward a reference model. 
We demonstrate that MNPO inherits the equilibrium guarantees of two-player methods while enabling richer competitive dynamics and improved coverage of diverse preference structures. Comprehensive empirical evaluation shows that MNPO consistently outperforms existing NLHF baselines on instruction-following benchmarks, achieving superior alignment quality under heterogeneous annotator conditions and mixed-policy evaluation scenarios. Together, these results establish MNPO as a principled and scalable framework for aligning LLMs with complex, non-transitive human preferences. Code is available at~\url{https://github.com/smiles724/MNPO}.
Falcon: Fast Proximal Linearization of Normalized Cuts for Unsupervised Image Segmentation
Authors: Xiao Zhang; Xiangyu Han; Xiwen Lai; Yao Sun; Pei Zhang; Xia Liu; Konrad Kording
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Current zero-shot unsupervised segmentation methods based on normalized cuts (NCut) face three key limitations. First, they rely on recursive bipartitions with repeated eigen-decompositions, making them prohibitively expensive at scale. Second, each split requires spectral relaxation followed by rounding, introducing layers of approximation where the final partition may diverge from the true NCut objective. Third, recursive bipartitioning offers no principled assurance of producing a stable $K$-way segmentation, and existing heuristics lack convergence guarantees. We propose \textbf{Falcon}, a proximal-gradient solver that directly optimizes the discrete $K$-way NCut objective without spectral relaxation. We prove linear convergence under the \textit{Kurdyka--\L{}ojasiewicz} (KL) property. Falcon computes closed-form gradient scores weighted by cluster volumes and performs row-wise one-hot proximal updates stabilized by inertia. A monotone backtracking scheme adaptively tunes the proximal parameter, ensuring non-decreasing NCut values. This design preserves discrete feasibility, removes repeated eigen-decomposition, and guarantees convergence. Across six benchmarks, Falcon outperforms the strongest official baseline (DiffCut) by wide margins, e.g., +13.2 mIoU on VOC, +27.7 on COCO-Object, and +3.1 on Cityscapes, while remaining competitive on Pascal Context. It also runs up to an order of magnitude faster than recursive NCut and scales more favorably in memory at high resolution, making it practical for larger token grids. By pairing pretrained foundation models with a principled NCut solver, Falcon sets a new state of the art across six benchmarks and achieves the best performance on 17 of 18 benchmark--encoder pairs, underscoring both its robustness and its generality in bridging the gap between unsupervised and supervised segmentation.
Can Transformers Really Do It All? On the Compatibility of Inductive Biases Across Tasks
Authors: Damien Teney; Liangze Jiang; Hemanth Saratchandran; Simon Lucey
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Transformers are remarkably versatile and their design is largely consistent across a variety of applications. But are they optimal for any given task or dataset? The answer may be key for pushing AI beyond merely scaling current designs.

**Method.** We present a method to optimize a transformer architecture for a given dataset, which we use as a tool to study optimal task-specific inductive biases. This method replaces the most important non-linearities (GeLUs,;softmax) with functions learned on held-out data. We then train the resulting architectures on other datasets, as a way to evaluate the compatibility between pairs of tasks.

**Findings.** On algorithmic toy tasks, we identify new architectures with dramatic improvements in learning speed, in- and out-of-distribution generalization, and stability across seeds. The new designs prove very task-specific however, and indicate that these tasks require inductive biases very different from those of standard transformers. On code and language modeling datasets, we also find architectures with consistent, yet smaller improvements. These designs transfer much better across datasets and domains (English & computer code).

**Implications.** Our results show that standard transformers are rarely a local optimum in the space of architectures. Simple alternatives can perform much better but sacrifice universality. This suggests that there may be room for improved architectures that better support multiple capabilities simultaneously, such as fluency and robust reasoning.
Dissecting Representation Misalignment in Contrastive Learning via Influence Function
Authors: Huanyi Xie; Chenyang Ren; Khouloud Saadi; Shu Yang; Zhen Tan; Jingfeng Zhang; Lijie Hu; Di Wang
Venue: ICLR 2026 (ICLR 2026 Poster)

Abstract: Contrastive learning, commonly applied in large-scale multimodal models, often relies on data from diverse and often unreliable sources, which can include misaligned or mislabeled text-image pairs. This frequently leads to robustness issues and hallucinations, ultimately causing performance degradation. Data valuation is an efficient way to detect and trace these misalignments. Nevertheless, existing methods are computationally expensive for large-scale models. Although computationally efficient, classical influence functions are inadequate for contrastive learning models, as they were initially designed for pointwise loss. Furthermore, contrastive learning involves minimizing the distance between positive sample modalities while maximizing the distance between negative sample modalities. This necessitates evaluating the influence of samples from both perspectives. To tackle these challenges, we introduce the Extended Influence Function for Contrastive Loss (ECIF), an influence function crafted for contrastive loss. ECIF considers both positive and negative samples and provides a closed-form approximation of contrastive learning models, eliminating the need for retraining. Building upon ECIF, we develop a series of algorithms for data evaluation, misalignment detection, and misprediction trace-back tasks. Experimental results demonstrate that our ECIF advances the transparency and interpretability of CLIP-style embedding models by offering a more accurate assessment of data impact and model alignment compared to traditional baseline methods.