AI News Today

arXiv:2606.06256v2 Announce Type: replace Abstract: As the input length of large language model (LLM) serving continues to grow, the KV cache has become a dominant bottleneck in AI infrastructure. It limits GPU memory capacity, serving concurrency, cache reuse, and distributed scalability. Multiple important problems, including position-independent KV cache, prefix KV cache compression, hot/cold KV cache separation, and distributed KV cache management, all depend on how the KV cache is represent

arXiv:2606.07157v3 Announce Type: replace Abstract: Many efforts to ensure frontier AI models are safe rely on monitoring their chain-of-thought (CoT) reasoning. If models become able to perform sufficiently complex reasoning internally, without explicit thinking tokens, this would undermine such oversight. We measure how well frontier models reason without CoT across a suite of over 30,000 questions spanning 43 benchmarks in domains including math, coding, puzzles, causality, theory-of-mind, an

arXiv:2606.16974v3 Announce Type: replace Abstract: The reproducibility crisis has directed the AI research community toward improving documentation practices. Several studies have identified methodological issues, and in response, the most impactful venues in the field have introduced reproducibility checklists. We seek to understand whether documentation practices have changed over time by assessing all published papers at five leading AI conferences over the past decade. Seven reproducibility

arXiv:2606.22902v3 Announce Type: replace Abstract: Real-world users typically have access to multiple Large Language Models (LLMs) from different providers, and these LLMs often excel at distinct domains, yet none dominate all. Consequently, routing each task to the most suitable model becomes critical for both performance and cost. Existing routers treat this as a static, one-off classification problem. However, we identify the performance bottleneck for these routers as information deficit: s

arXiv:2606.23533v2 Announce Type: replace Abstract: Recent large language models (LLMs) are good at general text generation, but it is still hard to use them for domain-specific data generation because the output must follow strict formatting and structural rules. Unlike open-ended tasks such as question answering or translation, domain-specific generation must be both semantically correct and compliant with existing guidelines and standards. In this work, we study the nationwide interoperabilit

arXiv:2605.01718v2 Announce Type: replace Abstract: Unlearnable examples (UEs) aim to compromise model training by injecting imperceptible perturbations to clean samples. However, existing UE schemes exhibit limited robustness against advanced defenses due to their heuristic design or narrowly scoped domain perturbations. To address this, we propose \texttt{DUNE}, a \underline{\textbf{D}}ual-branch \underline{\textbf{UN}}learnable \underline{\textbf{E}}nsemble perturbation optimization approach.

arXiv:2605.25042v2 Announce Type: replace Abstract: Existing score-based methods for inverse problems often resort to approximate minimization of the KL divergence between the inversion distribution and the Bayesian posterior. Such an approximation leads to severe mode collapse and unreliable uncertainty quantification. In this paper, we propose Principled Posterior Matching (PPM), a framework that returns to the fundamentals of variational inference, rather than using tricky approximations. Ins

arXiv:2606.01920v2 Announce Type: replace Abstract: Diffusion-based dataset distillation has recently emerged as a promising paradigm for condensing large-scale datasets into compact synthetic sets. By leveraging pretrained generative priors, these methods can produce realistic class-conditional samples more efficiently than traditional matching-based approaches. However, most existing diffusion-based methods still adopt a rigid ``Generate-and-Use'' strategy, where the generated samples are dire

arXiv:2606.02379v2 Announce Type: replace Abstract: Metric scale monocular geometry estimation has seen significant progress through large-scale data aggregation, yet current foundation models suffer from a persistent ''scale-collapse'' phenomenon: distant landmarks and vast landscapes are metrically underestimated. We hypothesize that this performance gap stems from a training data bottleneck, where existing metric-scale datasets are hardware-constrained to homogenous vehicle-captured LiDAR or

arXiv:2606.10874v2 Announce Type: replace Abstract: In quantum image processing, a fundamental step is encoding classical image data into quantum states. This can be achieved using methods such as Flexible Representation of Quantum Images (FRQI), Quantum Probability Image Encoding (QPIE), and Novel Enhanced Quantum Representation (NEQR). However, on real quantum hardware, these encodings can quickly lead to circuits with many gates, large circuit depth, and high qubit usage, which is a problem f

arXiv:2606.10876v2 Announce Type: replace Abstract: Illegal logging and timber trade continue to pose significant challenges in the Philippines, where accurate wood species identification is essential for enforcement but limited by the need for specialised equipment and expertise. This study aims to evaluate whether AI models for macroscopic wood identification can be developed and deployed by wood scientists without programming expertise using the Xylorix platform, focusing on five Philippine h

arXiv:2503.13051v3 Announce Type: replace-cross Abstract: Sorting and permutation learning are key concepts in optimization and machine learning, especially when organizing high-dimensional data into meaningful spatial layouts. The Gumbel-Sinkhorn method, while effective, requires N*N parameters to determine a full permutation matrix, making it computationally expensive for large datasets. Low-rank matrix factorization approximations reduce memory requirements to 2NM (with M << N), but they stil

arXiv:2602.17901v3 Announce Type: replace-cross Abstract: Self-supervised learning (SSL) and diffusion models have respectively advanced representation learning and generative modeling for high-dimensional 3D visual data, yet they are often developed as separate paradigms. Their unification remains challenging under multi-source heterogeneity, as anatomical content must be preserved for analysis while acquisition-related style varies across centers and affects synthesis. In this paper, we propos

arXiv:2602.23754v3 Announce Type: replace-cross Abstract: We present Neural Image Space Tessellation effect (NIST), a lightweight screen-space post-processing approach for reducing the faceted silhouettes of low-poly renderings. Instead of tessellating primitives, creating new geometry, or modifying the underlying mesh, NIST uses the low-poly rendering result together with simple auxiliary G-buffer attributes to learn geometry-guided smoothing of object contours in image space. At its core, NIST

arXiv:2409.13007v3 Announce Type: replace Abstract: Class imbalance poses a significant challenge in classification tasks, often causing standard learning algorithms to become biased toward the majority class. Cost-sensitive learning (CSL) addresses this issue by assigning higher penalties to minority-class misclassifications. However, conventional CSL typically applies a uniform penalty to all minority-class instances, ignoring the fact that minority samples may differ substantially in terms of

arXiv:2501.07400v2 Announce Type: replace Abstract: We derive explicit equations governing the cumulative biases and weights in Deep Learning with ReLU activation function, based on gradient descent for the Euclidean loss in the input layer, and under the assumption that the weights are, in a precise sense, adapted to the coordinate system distinguished by the activations. We show that gradient descent corresponds to a dynamical process in the input layer, whereby clusters of data are progressiv

arXiv:2508.03898v2 Announce Type: replace Abstract: Accurate prediction of grape phenology is essential for timely vineyard management decisions, such as scheduling irrigation and fertilization, to maximize crop yield and quality. While traditional biophysical models calibrated on historical field data can be used for season-long predictions, they lack the precision required for fine-grained vineyard management. Deep learning methods are a compelling alternative but their performance is hindered

arXiv:2510.03243v3 Announce Type: replace Abstract: Efficient scheduling of large language model (LLM) inference tasks is critical for achieving low latency and high throughput, a challenge that is becoming increasingly acute with the rise of reasoning-capable LLMs whose generation lengths are highly variable. Traditional strategies like First Come, First-Serve (FCFS) often suffer from Head-of-Line (HOL) blocking, where long-running tasks delay shorter ones queued behind them. In this paper, we

arXiv:2510.09685v2 Announce Type: replace Abstract: Deep learning has become a pivotal technology in fields such as computer vision, scientific computing, and dynamical systems, significantly advancing these disciplines. However, neural Networks persistently face challenges related to theoretical understanding, interpretability, and generalization. To address these issues, researchers are increasingly adopting a differential equations perspective to propose a unified theoretical framework and sy

arXiv:2512.23075v5 Announce Type: replace Abstract: Policy gradient methods for Large Language Models optimize a policy $\pi_\theta$ via a surrogate objective computed from samples of a rollout policy $\pi_{\text{roll}}$. However, modern LLM-RL pipelines suffer from unavoidable implementation divergences -- backend discrepancies, Mixture-of-Experts routing discontinuities, and distributed training staleness -- causing off-policy mismatch ($\pi_{\text{roll}} \neq \pi_\theta$) and approximation er

arXiv:2601.16632v5 Announce Type: replace Abstract: Time series forecasting has witnessed significant progress with deep learning. While prevailing approaches enhance forecasting performance by modifying architectures or introducing novel enhancement strategies, they often fail to dynamically disentangle and leverage the complex, intertwined temporal patterns inherent in time series, thus resulting in the learning of static, averaged representations that lack context-aware capabilities. To addre

arXiv:2602.16065v2 Announce Type: replace Abstract: As artificial intelligence (AI)-generated content proliferates, models are increasingly trained on their own outputs, risking progressive degradation or collapse. In this article, we provide the first positive, rigorous theoretical results, to the best of our knowledge, showing that under model-agnostic mild conditions, the model converges to the true data-generating distribution. The convergence rate is the minimum of the model's intrinsic rat

arXiv:2605.27482v2 Announce Type: replace Abstract: While orthogonal subspace methods try to mitigate task interference in Continual Learning (CL), they often suffer from energy diffusion across the basis, hindering knowledge compaction and exhausting capacity for future tasks. We observe that output feature drift induced by parameter updates is inherently low-rank, and theoretically prove that preserving parameters along the principal directions of this drift minimizes the output reconstruction

arXiv:2605.30719v2 Announce Type: replace Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for reinforcement learning (RL) tasks, i.e., when can we replace classical RL algorithms with an LLM? We explore this question by introducing Prompted Policy Optimization (PromptPO), an iterative method that prompts an LLM with Python descriptions of the state space, action space, and reward function, then has it generate and refine executable policies

arXiv:2606.21641v2 Announce Type: replace Abstract: Large language models (LLMs) have been proposed as hyperparameter-optimization (HPO) advisors that "warm-start" search from prior knowledge, proposing strong configurations in very few evaluations. We test that claim under a budget-matched, multi-seed protocol on eight PMLB tabular benchmarks, comparing an LLM advisor (LLM-OptFlow) against four classical baselines (random search, Optuna-TPE, Gaussian-process Bayesian optimization, and successiv

<!-- SC_OFF --><div class="md"><p>Hi all,</p> <p>I’m working on a research project exploring how stateless LLM-based chatbots handle long conversations and whether important earlier information is still reliably retained over time.</p> <p>My idea is to:</p> <ul> <li>Run a chatbot using an LLM API without any external memory system</li> <li>Introduce key facts early in a long conversation</li> <li>Continue with many unrelated messages (hundreds of turns)</li> <li>Later test whether the model can

<!-- SC_OFF --><div class="md"><p>I've been teaching myself how LLMs actually work, not at the API level, but down to the matrix multiplications. To force myself to really understand the forward pass, I first built a complete transformer by hand in a spreadsheet from embeddings through to the loss. Then I turned the forward pass into a web page so it's easier to share.</p> <p>It's a full transformer (single attention head, single block) shrunk to the smallest size where every single number still

<!-- SC_OFF --><div class="md"><p>Hey guys,</p> <p>I was playing around with Nanotron recently and got super frustrated by how many heavy, hardware-specific dependencies it imports at the module level ( flash-attn , triton, functorch , etc.). If you try to run it on older or budget GPUs like a T4 or V100, it just crashes on import.</p> <p>So I wrote Picotron (<a href="https://github.com/Syntropy-AI-Labs/picotron">https://github.com/Syntropy-AI-Labs/picotron</a>) to solve this. It's a clean-room

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1uh61uw/hiding_messages_in_the_least_significant_mantissa/"> <img src="https://external-preview.redd.it/xL20TWLoDXtutsGuMHS1qdEyNEn6zkliHGGNaYV1H4A.png?width=640&crop=smart&auto=webp&s=2b035893689551e28412391b858ab5c0323b052d" alt="Hiding messages in the least significant mantissa bits of fine-tuned ONNX model weights [P]" title="Hiding messages in the least significant mantissa bits of fine-tuned ONNX model weights [P]"

There are over 7,000 natural languages today, but that doesn’t stop people from occasionally making up completely new ones. These constructed languages, or conlangs , include Dothraki , Klingon , and various Elvish languages . Now, an AI model called ConlangCrafter is also capable of generating new languages—and it is particularly good at it. In a paper published 27 June in the Proceedings of the Association of Computer Linguists, researchers analyzed ConlangCrafter’s language generation abiliti

<!-- SC_OFF --><div class="md"><p>Hey all, a bit of background - I'm an ex Amateur MMA fighter and BJJ brown belt and am also in the AI/ML space ... weird combo but wanted to know if anyone else was at the intersection of ML/AI and MMA/BJJ.</p> <p>In short, I'm building AI models that "watch" fights and are able to detect positions and moments throughout the fights - things like standing vs clinching vs ground (with intention of becoming more granular in time) along with detecting knockdowns, ta

<!-- SC_OFF --><div class="md"><p>Hey !<br/> I’m starting a series to document my work on GPU infrastructure, LLMs, and CV.</p> <p><a href="https://medium.com/@kenansuleyman/gpu-journey-1-the-silicon-that-ate-the-world-b211fd142835">Stop #1 is up:</a> A brief look at why GPUs are the center of the industry, the CPU/GPU divide, and why <code>nvidia-smi</code> is the first place you check when things break.</p> <p>We’ll move past the basics quickly to focus on:</p> <ul> <li>Empirical architecture

<!-- SC_OFF --><div class="md"><p>It is like pytest but for statistical tests: it ensures no regression of your metrics at a statistical level.</p> <p>It manages tedious things such that seeds, past benchmark results, ...</p> <p>Simple CLI working like pytest but with benchmarks/ directory instead of tests/:</p> <pre><code>pybench # 1st time: samples seeds, saves a baseline, marks NEW pybench # later: reruns on the same seeds, marks PASS / FAIL pybench update # re-baseline after an intended chan

Machine Intelligence

<table> <tr><td> <a href="https://www.reddit.com/r/MachineLearning/comments/1uga687/a_debugger_for_rl_reward_functions_that_detects/"> <img src="https://preview.redd.it/r5m95bf5cn9h1.gif?width=640&crop=smart&s=f9e1900b5e007ea3a72c74d4089c56fdeed22f49" alt="A debugger for RL reward functions that detects reward hacking during training [P]" title="A debugger for RL reward functions that detects reward hacking during training [P]" /> </a> </td><td> <!-- SC_OFF --><div class="md"><p>While experiment

<!-- SC_OFF --><div class="md"><p>My question on live continual learning use cases was removed by moderators here because they think i asked basic level question about live continual learning which i thought is a frontier level research. But anyways. Is anyone interested in talking about continual learning (live) and catastrophic forgetting? </p> </div><!-- SC_ON --> submitted by <a href="https://www.reddit.com/user/fourwheels2512"> /u/fourwheels2512 </a> <br/> <span><a href="https://www.reddit.

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Heat waves mess with your brain. Scientists are trying to figure out why. —Jessica Hamzelou It’s been hot in London this week. Really hot. A dangerous heat wave has hit Western…

arXiv:2606.26158v1 Announce Type: new Abstract: When a benchmark's accuracy saturates, it is often retired and replaced with a more challenging version. We show that this approach privileges accuracy and misses the opportunity to study six other key dimensions of agent performance: construct validity issues such as shortcuts, out-of-distribution generalizability, efficiency, reliability, the relative importance of the model versus the scaffold, and uplift from human-agent collaboration. We use C

arXiv:2606.26161v1 Announce Type: new Abstract: Linear directions in activation space have been identified for both refusal and persona traits in instruction-tuned chat models, but the two have been studied as separate mechanisms. We show they interact: a compliant persona gates refusal. In Qwen2.5-7B-Instruct and Llama-3.1-8B-Instruct, we extract a compliant model-persona direction and a refusal direction and intervene on both. Compliant persona steering suppresses refusal -- in Llama, the refu

arXiv:2606.26173v1 Announce Type: new Abstract: Recent work shows that Large Language Models (LLMs) can act as semantic mutation operators for the evolutionary discovery of programs and proofs. Most current applications focus on static coding benchmarks. We extend this paradigm to algorithmic trading. This domain is uniquely challenging because it is noisy, non-stationary, and highly discontinuous. We present AlgoEvolve, an LLM-driven evolutionary framework that generates, evaluates, and iterati