arXiv:2601.21192v1 Announce Type: new Abstract: State-of-the-art embedding models are increasingly derived from decoder-only Large Language Model (LLM) backbones adapted via contrastive learning. Given the emergence of reasoning models trained via Reinforcement Learning with Verifiable Rewards (RLVR), a natural question arises: do enhanced reasoning translate to superior semantic representations when these models serve as embedding initializations? […]
End-to-end audio-visual learning for cochlear implant sound coding simulations in noisy environments
arXiv:2508.13576v2 Announce Type: replace-cross Abstract: The cochlear implant (CI) is a successful biomedical device that enables individuals with severe-to-profound hearing loss to perceive sound through electrical stimulation, yet listening in noise remains challenging. Recent deep learning advances offer promising potential for CI sound coding by integrating visual cues. In this study, an audio-visual speech enhancement […]
When should I search more: Adaptive Complex Query Optimization with Reinforcement Learning
arXiv:2601.21208v1 Announce Type: new Abstract: Query optimization is a crucial component for the efficacy of Retrieval-Augmented Generation (RAG) systems. While reinforcement learning (RL)-based agentic and reasoning methods have recently emerged as a promising direction on query optimization, most existing approaches focus on the expansion and abstraction of a single query. However, complex user queries are […]
On-the-Fly Adaptation to Quantization: Configuration-Aware LoRA for Efficient Fine-Tuning of Quantized LLMs
arXiv:2509.25214v2 Announce Type: replace-cross Abstract: As increasingly large pre-trained models are released, deploying them on edge devices for privacy-preserving applications requires effective compression. Recent works combine quantization with the fine-tuning of high-precision LoRA adapters, which can substantially reduce model size while mitigating the accuracy loss from quantization. However, edge devices have inherently heterogeneous capabilities, while […]
Uncovering Hidden Correctness in LLM Causal Reasoning via Symbolic Verification
arXiv:2601.21210v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly being applied to tasks that involve causal reasoning. However, current benchmarks often rely on string matching or surface-level metrics that do not capture whether the output of a model is formally valid under the semantics of causal reasoning. To address this, we propose DoVerifier, […]
What the flock knows that the birds do not: exploring the emergence of joint agency in multi-agent active inference
arXiv:2511.10835v2 Announce Type: replace-cross Abstract: Collective behavior pervades biological systems, from flocks of birds to neural assemblies and human societies. Yet, how such collectives acquire functional properties — such as joint agency or knowledge — that transcend those of their individual components remains an open question. Here, we combine active inference and information-theoretic analyses to […]
Fairy2i: Training Complex LLMs from Real LLMs with All Parameters in $\pm 1, pm i$
arXiv:2512.02901v3 Announce Type: replace-cross Abstract: Large language models (LLMs) have revolutionized artificial intelligence, yet their massive memory and computational demands necessitate aggressive quantization, increasingly pushing representations toward the theoretical limit of a single bit. While complex-valued LLMs, such as iFairy, offer a superior chance for low-bit representation compared to real-valued counterparts, they require training from […]
Large Vision Models Can Solve Mental Rotation Problems
arXiv:2509.15271v2 Announce Type: replace-cross Abstract: Mental rotation is a key test of spatial reasoning in humans and has been central to understanding how perception supports cognition. Despite the success of modern vision transformers, it is still unclear how well these models develop similar abilities. In this work, we present a systematic evaluation of ViT, CLIP, […]
Rethinking LLM-Driven Heuristic Design: Generating Efficient and Specialized Solvers via Dynamics-Aware Optimization
arXiv:2601.20868v1 Announce Type: cross Abstract: Large Language Models (LLMs) have advanced the field of Combinatorial Optimization through automated heuristic generation. Instead of relying on manual design, this LLM-Driven Heuristic Design (LHD) process leverages LLMs to iteratively generate and refine solvers to achieve high performance. However, existing LHD frameworks face two critical limitations: (1) Endpoint-only evaluation, […]
PROMA: Projected Microbatch Accumulation for Reference-Free Proximal Policy Updates
arXiv:2601.10498v3 Announce Type: replace-cross Abstract: This note introduces Projected Microbatch Accumulation (PROMA), a proximal policy method that modifies gradient accumulation across microbatches rather than relying on likelihood ratios relative to a reference policy. During accumulation, PROMA projects the partially accumulated gradient to be orthogonal to the sequence-wise gradients of the current microbatch. This projection is […]
TACLer: Tailored Curriculum Reinforcement Learning for Efficient Reasoning
arXiv:2601.21711v1 Announce Type: cross Abstract: Large Language Models (LLMs) have shown remarkable performance on complex reasoning tasks, especially when equipped with long chain-of-thought (CoT) reasoning. However, eliciting long CoT typically requires large-scale reinforcement learning (RL) training, while often leading to overthinking with redundant intermediate steps. To improve learning and reasoning efficiency, while preserving or even […]
Residual Reservoir Memory Networks
arXiv:2508.09925v2 Announce Type: replace-cross Abstract: We introduce a novel class of untrained Recurrent Neural Networks (RNNs) within the Reservoir Computing (RC) paradigm, called Residual Reservoir Memory Networks (ResRMNs). ResRMN combines a linear memory reservoir with a non-linear reservoir, where the latter is based on residual orthogonal connections along the temporal dimension for enhanced long-term propagation […]