GenAI4ELab/papercli-papers huggingface dataset:
Shared-AE: Automatic Identification of Shared Subspaces in High-dimensional Neural and Behavioral Activity
Authors: Daiyao Yi; Hao Dong; Michael James Higley; Anne Churchland; Shreya Saxena
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Understanding the relationship between behavior and neural activity is crucial for understanding brain function. An effective method is to learn embeddings for interconnected modalities. For simple behavioral tasks, neural features can be learned based on labels. However, complex behaviors, such as social interactions, require the joint extraction of behavioral and neural characteristics. In this paper, we present an autoencoder (AE) framework, called Shared-AE, which includes a novel regularization term that automatically identifies features shared between neural activity and behavior, while simultaneously capturing the unique private features specific to each modality. We apply Shared-AE to large-scale neural activity recorded across the entire dorsal cortex of the mouse, during two very different behaviors: (i) head-fixed mice performing a self-initiated decision-making task, and (ii) freely-moving social behavior amongst two mice. Our model successfully captures both `shared features', shared across neural and behavioral activity, and `private features', unique to each modality, significantly enhancing our understanding of the alignment between neural activity and complex behaviors. The original code for the entire Shared-AE framework on Pytorch has been made publicly available at: \url{https://github.com/saxenalab-neuro/Shared-AE}.
Bounds on $L_p$ Errors in Density Ratio Estimation via $f$-Divergence Loss Functions
Authors: Yoshiaki Kitazawa
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Density ratio estimation (DRE) is a core technique in machine learning used to capture relationships between two probability distributions. 
$f$-divergence loss functions, which are derived from variational representations of $f$-divergence, have become a standard choice in DRE for achieving cutting-edge performance. 
This study provides novel theoretical insights into DRE by deriving upper and lower bounds on the $L_p$ errors through $f$-divergence loss functions. 
These bounds apply to any estimator belonging to a class of Lipschitz continuous estimators, irrespective of the specific $f$-divergence loss function employed.
The derived bounds are expressed as a product involving the data dimensionality and the expected value of the density ratio raised to the $p$-th power.
Notably, the lower bound includes an exponential term that depends on the Kullback--Leibler (KL) divergence, revealing that the $L_p$ error increases significantly as the KL divergence grows when $p > 1$. 
This increase becomes even more pronounced as the value of $p$ grows. 
The theoretical insights are validated through numerical experiments.
Palu: KV-Cache Compression with Low-Rank Projection
Authors: Chi-Chih Chang; Wei-Cheng Lin; Chien-Yu Lin; Chong-Yan Chen; Yu-Fang Hu; Pei-Shuo Wang; Ning-Chi Huang; Luis Ceze; Mohamed S. Abdelfattah; Kai-Chiang Wu
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Post-training KV-Cache compression methods typically either sample a subset of effectual tokens or quantize the data into lower numerical bit width. However, these methods cannot exploit redundancy in the hidden dimension of the KV tenors. This paper presents a hidden dimension compression approach called Palu, a KV-Cache compression framework that utilizes low-rank projection to reduce inference-time LLM memory usage. Palu decomposes the linear layers into low-rank matrices, caches compressed intermediate states, and reconstructs the full keys and values on the fly. To improve accuracy, compression rate, and efficiency, Palu further encompasses (1) a medium-grained low-rank decomposition scheme, (2) an efficient rank search algorithm, (3) low-rank-aware quantization compatibility enhancements, and (4) an optimized GPU kernel with matrix fusion. Extensive experiments with popular LLMs show that Palu compresses KV-Cache by 50% while maintaining strong accuracy and delivering up to 1.89× speedup on the RoPE-based attention module. When combined with quantization, Palu’s
inherent quantization-friendly design yields small to negligible extra accuracy degradation while saving additional memory than quantization-only methods and achieving up to 2.91× speedup for the RoPE-based attention. Moreover, it maintains comparable or even better accuracy (up to 1.19 lower perplexity) compared to quantization-only methods. These results demonstrate Palu’s superior capability to effectively address the efficiency and memory challenges of LLM inference posed by KV-Cache. Our code is publicly available at: https://github.com/shadowpa0327/Palu.
Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling
Authors: Liliang Ren; Yang Liu; Yadong Lu; yelong shen; Chen Liang; Weizhu Chen
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Efficiently modeling sequences with infinite context length has long been a challenging problem. Previous approaches have either suffered from quadratic computational complexity or limited extrapolation ability in length generalization. In this
work, we present Samba, a simple hybrid architecture that layer-wise combines
Mamba, a selective State Space Model (SSM), with Sliding Window Attention
(SWA). Samba selectively compresses a given sequence into recurrent hidden
states while still maintaining the ability to precisely recall recent memories with the
attention mechanism. We scale Samba up to 3.8B parameters with 3.2T training
tokens and demonstrate that it significantly outperforms state-of-the-art models
across a variety of benchmarks. Pretrained on sequences of 4K length, Samba
shows improved perplexity in context lengths of up to 1M in zero-shot. When
finetuned on 4K-length sequences, Samba efficiently extrapolates to a 256K context length with perfect memory recall on the Passkey Retrieval task, and exhibits
superior retrieval extrapolation on the challenging Phonebook task compared to
full-attention models. As a linear-time sequence model, Samba achieves a 3.73×
higher throughput compared to Transformers with grouped-query attention for user
prompts of 128K length, and a 3.64× speedup when generating 64K tokens with
unlimited streaming.
Nova: Generative Language Models for Assembly Code with Hierarchical Attention and Contrastive Learning
Authors: Nan Jiang; Chengxiao Wang; Kevin Liu; Xiangzhe Xu; Lin Tan; Xiangyu Zhang; Petr Babkin
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Binary code analysis is the foundation of crucial tasks in the security domain; thus building effective binary analysis techniques is more important than ever. Large language models (LLMs) although have brought impressive improvement to source code tasks, do not directly generalize to assembly code due to the unique challenges of assembly: (1) the low information density of assembly and (2) the diverse optimizations in assembly code. To overcome these challenges, this work proposes a hierarchical attention mechanism that builds attention summaries to capture the semantics more effectively and designs contrastive learning objectives to train LLMs to learn assembly optimization. Equipped with these techniques, this work develops Nova, a generative LLM for assembly code. Nova outperforms existing techniques on binary code decompilation by up to 14.84 -- 21.58% higher Pass@1 and Pass@10, and outperforms the latest binary code similarity detection techniques by up to 6.17% Recall@1, showing promising abilities on both assembly generation and understanding tasks.
Reducing Hallucinations in Large Vision-Language Models via Latent Space Steering
Authors: Sheng Liu; Haotian Ye; James Zou
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Hallucination poses a challenge to the deployment of large vision-language models (LVLMs) in applications. Unlike in large language models (LLMs), hallucination in LVLMs often arises from misalignments between visual inputs and textual outputs. This paper investigates the underlying mechanisms of hallucination, focusing on the unique structure of LVLMs that distinguishes them from LLMs. We identify that hallucinations often arise from the sensitivity of text decoders to vision inputs, a natural phenomenon when image encoders and text decoders are pre-trained separately. Inspired by this, we introduce Visual and Textual Intervention (VTI), a novel technique designed to reduce hallucinations by steering latent space representations during inference to enhance the stability of vision features. As a task-agnostic test-time intervention, VTI can be easily applied to any problem without additional training costs. Extensive experiments demonstrate that it can effectively reduce hallucinations and outperform baseline methods across multiple metrics, highlighting the critical role of vision feature stability in LVLMs.
Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
Authors: Jasmine Bayrooti; Carl Henrik Ek; Amanda Prorok
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Learning complex robot behavior through interactions with the environment necessitates principled exploration. Effective strategies should prioritize exploring regions of the state-action space that maximize rewards, with optimistic exploration emerging as a promising direction aligned with this idea and enabling sample-efficient reinforcement learning. However, existing methods overlook a crucial aspect: the need for optimism to be informed by a belief connecting the reward and state. To address this, we propose a practical, theoretically grounded approach to optimistic exploration based on Thompson sampling.  Our approach is the first that allows for reasoning about _joint_ uncertainty over transitions and rewards for optimistic exploration. We apply our method on a set of MuJoCo and VMAS continuous control tasks. Our experiments demonstrate that optimistic exploration significantly accelerates learning in environments with sparse rewards, action penalties, and difficult-to-explore regions. Furthermore, we provide insights into when optimism is beneficial and emphasize the critical role of model uncertainty in guiding exploration.
Mitigating Memorization in Language Models
Authors: Mansi Sakarvadia; Aswathy Ajith; Arham Mushtaq Khan; Nathaniel C Hudson; Caleb Geniesse; Kyle Chard; Yaoqing Yang; Ian Foster; Michael W. Mahoney
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Language models (LMs) can “memorize” information, i.e., encode training data in their weights in such a way that inference-time queries can lead to verbatim regurgitation of that data. This ability to extract training data can be problematic, for example, when data are private or sensitive. In this work, we investigate methods to mitigate memorization: three regularizer-based, three fine-tuning-based, and eleven machine unlearning-based methods, with five of the latter being new methods that we introduce. We also introduce TinyMem, a suite of small, computationally-efficient LMs for the rapid development and evaluation of memorization-mitigation methods. We demonstrate that the mitigation methods that we develop using TinyMem can successfully be applied to production-grade LMs, and we determine via experiment that: regularizer-based mitigation methods are slow and ineffective at curbing memorization; fine-tuning-based methods
are effective at curbing memorization, but overly expensive, especially for retaining higher accuracies; and unlearning-based methods are faster and more effective, allowing for the precise localization and removal of memorized information from LM weights prior to inference. We show, in particular, that our proposed unlearning method BalancedSubnet outperforms other mitigation methods at removing
memorized information while preserving performance on target tasks.
Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement Learning
Authors: Prajwal Koirala; Zhanhong Jiang; Soumik Sarkar; Cody Fleming
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: In safe offline reinforcement learning, the objective is to develop a policy that maximizes cumulative rewards while strictly adhering to safety constraints, utilizing only offline data. Traditional methods often face difficulties in balancing these constraints, leading to either diminished performance or increased safety risks. We address these issues with a novel approach that begins by learning a conservatively safe policy through the use of Conditional Variational Autoencoders, which model the latent safety constraints. Subsequently, we frame this as a Constrained Reward-Return Maximization problem, wherein the policy aims to optimize rewards while complying with the inferred latent safety constraints. This is achieved by training an encoder with a reward-Advantage Weighted Regression objective within the latent constraint space. Our methodology is supported by theoretical analysis, including bounds on policy performance and sample complexity. Extensive empirical evaluation on benchmark datasets, including challenging autonomous driving scenarios, demonstrates that our approach not only maintains safety compliance but also excels in cumulative reward optimization, surpassing existing methods. Additional visualizations provide further insights into the effectiveness and underlying mechanisms of our approach.
Large (Vision) Language Models are Unsupervised In-Context Learners
Authors: Artyom Gadetsky; Andrei Atanov; Yulun Jiang; Zhitong Gao; Ghazal Hosseini Mighan; Amir Zamir; Maria Brbic
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Recent advances in large language and vision-language models have enabled zero-shot inference, allowing models to solve new tasks without task-specific training. Various adaptation techniques such as prompt engineering, In-Context Learning (ICL), and supervised fine-tuning can further enhance the model’s performance on a downstream task, but they require substantial manual effort to construct effective prompts or labeled examples. In this work, we introduce a joint inference framework for fully unsupervised adaptation, eliminating the need for manual prompt engineering and labeled examples. Unlike zero-shot inference, which makes independent predictions, the joint inference makes predictions simultaneously for all inputs in a given task. Since direct joint inference involves computationally expensive optimization, we develop efficient approximation techniques, leading to two unsupervised adaptation methods: unsupervised fine-tuning and unsupervised ICL. We demonstrate the effectiveness of our methods across diverse tasks and models, including language-only Llama-3.1 on natural language processing tasks, reasoning-oriented Qwen2.5-Math on grade school math problems, vision-language OpenFlamingo on vision tasks, and the API-only access GPT-4o model on massive multi-discipline tasks. Our experiments demonstrate substantial improvements over the standard zero-shot approach, including 39% absolute improvement on the challenging GSM8K math reasoning dataset. Remarkably, despite being fully unsupervised, our framework often performs on par with supervised approaches that rely on ground truth labels.
Effective post-training embedding compression via temperature control in contrastive training
Authors: Georgiana Dinu; Corey D Barrett; Yi Xiang; Miguel Romero Calvo; Anna Currey; Xing Niu
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Fixed-size learned representations (dense representations, or embeddings) are widely used in many machine learning applications across language, vision or speech modalities. This paper investigates the role of the temperature parameter in contrastive training for text embeddings. We shed light on the impact this parameter has on the intrinsic dimensionality of the embedding spaces obtained, and show that lower intrinsic dimensionality is further correlated with effective compression of embeddings. We still observe a trade-off between absolute performance and effective compression and we propose temperature aggregation methods which reduce embedding size by an order of magnitude with minimal impact on quality.
LLMs Can Plan Only If We Tell Them
Authors: Bilgehan Sel; Ruoxi Jia; Ming Jin
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Large language models (LLMs) have demonstrated significant capabilities in natural language processing and reasoning, yet their effectiveness in autonomous planning has been under debate. While existing studies have utilized LLMs with external feedback mechanisms or in controlled environments for planning, these approaches often involve substantial computational and development resources due to the requirement for careful design and iterative backprompting. Moreover, even the most advanced LLMs like GPT-4 struggle to match human performance on standard planning benchmarks, such as the Blocksworld, without additional support. This paper investigates whether LLMs can independently generate long-horizon plans that rival human baselines. Our novel enhancements to Algorithm-of-Thoughts (AoT), which we dub AoT+, help achieve state-of-the-art results in planning benchmarks out-competing prior methods and human baselines all autonomously.
Feature Responsiveness Scores: Model-Agnostic Explanations for Recourse
Authors: Seung Hyun Cheon; Anneke Wernerfelt; Sorelle Friedler; Berk Ustun
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Machine learning models routinely automate decisions in applications like lending and hiring. In such settings, consumer protection rules require companies that deploy models to explain predictions to decision subjects. These rules are motivated, in part, by the belief that explanations can promote *recourse* by revealing information that individuals can use to contest or improve their outcomes. In practice, many companies comply with these rules by providing individuals with a list of the most important features for their prediction, which they identify based on feature importance scores from feature attribution methods such as SHAP or LIME. In this work, we show how these practices can undermine consumers by highlighting features that would not lead to an improved outcome and by explaining predictions that cannot be changed. We propose to address these issues by highlighting features based on their *responsiveness score*—i.e., the probability that an individual can attain a target prediction by changing a specific feature. We develop efficient methods to compute responsiveness scores for any model and any dataset. We conduct an extensive empirical study on the responsiveness of explanations in lending. Our results show that standard practices in consumer finance can backfire by presenting consumers with *reasons without recourse*, and demonstrate how our approach improves consumer protection by highlighting responsive features and identifying fixed predictions.
InterMask: 3D Human Interaction Generation via Collaborative Masked Modeling
Authors: Muhammad Gohar Javed; chuan guo; Li cheng; Xingyu Li
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Generating realistic 3D human-human interactions from textual descriptions remains a challenging task. Existing approaches, typically based on diffusion models, often produce results lacking realism and fidelity. In this work, we introduce *InterMask*, a novel framework for generating human interactions using collaborative masked modeling in discrete space. InterMask first employs a VQ-VAE to transform each motion sequence into a 2D discrete motion token map. Unlike traditional 1D VQ token maps, it better preserves fine-grained spatio-temporal details and promotes *spatial awareness* within each token. Building on this representation, InterMask utilizes a generative masked modeling framework to collaboratively model the tokens of two interacting individuals. This is achieved by employing a transformer architecture specifically designed to capture complex spatio-temporal inter-dependencies. During training, it randomly masks the motion tokens of both individuals and learns to predict them. For inference, starting from fully masked sequences, it progressively fills in the tokens for both individuals. With its enhanced motion representation, dedicated architecture, and effective learning strategy, InterMask achieves state-of-the-art results, producing high-fidelity and diverse human interactions. It outperforms previous methods, achieving an FID of $5.154$ (vs $5.535$ of in2IN) on the InterHuman dataset and $0.399$ (vs $5.207$ of InterGen) on the InterX dataset. Additionally, InterMask seamlessly supports reaction generation without the need for model redesign or fine-tuning.
Denoising Autoregressive Transformers for Scalable Text-to-Image Generation
Authors: Jiatao Gu; Yuyang Wang; Yizhe Zhang; Qihang Zhang; Dinghuai Zhang; Navdeep Jaitly; Joshua M. Susskind; Shuangfei Zhai
Venue: ICLR 2025 (ICLR 2025 Poster)

Abstract: Diffusion models have become the dominant approach for visual generation. They are trained by denoising a Markovian process which gradually adds noise to the input. We argue that the Markovian property limits the model’s ability to fully utilize the generation trajectory, leading to inefficiencies during training and inference. In this paper, we propose DART, a transformer-based model that unifies autoregressive (AR) and diffusion within a non-Markovian framework.  DART iteratively denoises image patches spatially and spectrally using an AR model that has the same architecture as standard language models. DART does not rely on image quantization, which enables more effective image modeling while maintaining flexibility. Furthermore, DART seamlessly trains with both text and image data in a unified model. Our approach demonstrates competitive performance on class-conditioned and text-to-image generation tasks, offering a scalable, efficient alternative to traditional diffusion models. Through this unified framework, DART sets a new benchmark for scalable, high-quality image synthesis.
TopoNets: High performing vision and language models with brain-like topography
Authors: Mayukh Deb; Mainak Deb; Apurva Ratan Murty
Venue: ICLR 2025 (ICLR 2025 Spotlight)

Abstract: Neurons in the brain are organized such that nearby cells tend to share similar functions. AI models lack this organization, and past efforts to introduce topography have often led to trade-offs between topography and task performance. In this work, we present *TopoLoss*, a new loss function that promotes spatially organized topographic representations in AI models without significantly sacrificing task performance. TopoLoss is highly adaptable and can be seamlessly integrated into the training of leading model architectures. We validate our method on both vision (ResNet-18, ResNet-50, ViT) and language models (GPT-Neo-125M, NanoGPT), collectively *TopoNets*. TopoNets are the highest performing supervised topographic models to date, exhibiting brain-like properties such as localized feature processing, lower dimensionality, and increased efficiency. TopoNets also predict responses in the brain and replicate the key topographic signatures observed in the brain’s visual and language cortices, further bridging the gap between biological and artificial systems. This work establishes a robust and generalizable framework for integrating topography into AI, advancing the development of high performing models that more closely emulate the computational strategies of the human brain. Our project page: https://toponets.github.io