Semantic communication is emerging as a key enabler for distributed edge intelligence due to its capability to convey task-relevant meaning. However, achieving communication-efficient training and robust inference over wireless links remains challenging. This challenge is further exacerbated for multi-modal edge inference (MMEI) by two factors: 1) prohibitive communication overhead for distributed learning over bandwidth-limited wireless links, due to the emph{multi-modal} nature of the system; and 2) limited robustness under varying channels and noisy multi-modal inputs. In this paper, […]

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arXiv:2602.12470v1 Announce Type: new Abstract: RNA design, the task of finding a sequence that folds into a target secondary structure, has broad biological and biomedical impact but remains computationally challenging due to the exponentially large sequence space and exponentially many competing folds. Traditional approaches treat it as an optimization problem, relying on per-instance heuristics or constraint-based search. We instead reframe RNA design as conditional sequence generation and introduce a reusable neural approximator, instantiated as an autoregressive language model […]

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Motivated by auto-proof generation and Valiant’s VP vs. VNP conjecture, we study the problem of discovering efficient arithmetic circuits to compute polynomials, using addition and multiplication gates. We formulate this problem as a single-player game, where an RL agent attempts to build the circuit within a fixed number of operations. We implement an AlphaZero-style training loop and compare two approaches: Proximal Policy Optimization with Monte Carlo Tree Search (PPO+MCTS) and Soft Actor-Critic (SAC). SAC achieves the highest success […]

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Hey hey. Like the title says, we are currently optimizing our system (hive-mind/swarm-like collective) to be able to create great RL environments. And we are starting with pufferlib envs. It is doing a pretty damn good job atm. We are currently bootstrapped and we are limited on compute. Even a small batch of gpus, of a decent size, would be pretty wild for us. If you have any extra gpus laying around, or would potentially want to sponsor […]

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Execution is no longer the hard part. n Understanding is. AI has collapsed the cost of building, shipping, and iterating. n Code is faster. n Content is instant. n Decisions are suggested before we even ask for them. On the surface, this looks like progress. Underneath, it changes what actually breaks. Not the system. n The meaning inside it. Everyone talks about scaling execution. n Few design for shared understanding. That gap is where most modern product failures […]

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arXiv:2604.25025v1 Announce Type: new Abstract: Preference feedback, in the form of pairwise comparisons rather than scalar scores, has seen increasing use in applications such as human-, laboratory-, and expert-in-the-loop design, as well as scientific discovery. We propose a Thompson Sampling (TS) approach to Bayesian optimization with preferential feedback that models comparisons using a monotone link on latent utility differences and leverages the dueling kernel induced by a base kernel. We provide a finite-time analysis showing that the performance […]

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arXiv:2602.15195v1 Announce Type: new Abstract: LoRA adapters let users fine-tune large language models (LLMs) efficiently. However, LoRA adapters are shared through open repositories like Hugging Face Hub citep{huggingface_hub_docs}, making them vulnerable to backdoor attacks. Current detection methods require running the model with test input data — making them impractical for screening thousands of adapters where the trigger for backdoor behavior is unknown. We detect poisoned adapters by analyzing their weight matrices directly, without running the model — making […]

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arXiv:2603.24606v1 Announce Type: new Abstract: We present a method for estimating the number of distinct values (NDV) of a column in columnar file formats, using only existing file metadata–no extra storage, no data access. Two complementary signals are exploited: (1)~inverting the dictionary-encoded storage size equation yields accurate NDV estimates when distinct values are well-spread across row groups; (2)~counting distinct min/max values across row groups and inverting a coupon collector model provides robust estimates for sorted or partitioned data. […]

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This study investigates a method to guide and control fish schools using virtual fish trained with reinforcement learning. We utilize 2D virtual fish displayed on a screen to overcome technical challenges such as durability and movement constraints inherent in physical robotic agents. To address the lack of detailed behavioral models for real fish, we adopt a model-free reinforcement learning approach. First, simulation results show that reinforcement learning can acquire effective movement policies even when simulated real fish frequently […]

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arXiv:2601.08025v1 Announce Type: new Abstract: The deployment of deep neural networks (DNNs) on resource-constrained edge devices is frequently hindered by their significant computational and memory requirements. While partitioning and distributing a DNN across multiple devices is a well-established strategy to mitigate this challenge, prior research has largely focused on single-objective optimization, such as minimizing latency or maximizing throughput. This paper challenges that view by reframing DNN partitioning as a multi-objective optimization problem. We argue that in real-world scenarios, […]

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