AI NEWS JOURNAL

AI, IN PERSPECTIVE

Search AI news

Search headlines, summaries and articles to connect the dots.

2026-11-06

arXiv cs.AI13:00

Synthesizing symbolic models of human and machine reasoning at scale

arXiv:2611.07088v1 Announce Type: new Abstract: Symbolic models offer a powerful account of cognition, but their scope has historically been limited by the need for hand-engineering and computational tractability. We show how advances in artificial intelligence (AI) can scale symbolic modeling and help overcome these engineering limitations. We introduce Behavior-Conditioned Symbolic Synthesis (BCSS)---an approach for automated search of interpretable, deterministic symbolic programs that verifiably reproduce behavior---and apply it to model both human and machine behavior across hundreds of challenging reasoning problems. Analyzing these programs reveals that both people and machines tend to oversimplify, adopt rules that are partial explanations for their observations, and misinfer dependencies between problem features. These shared signatures are modulated by available resources, with reasoning effort predicting the extent to which machines simplify and explain their observations. Together, these results point to underlying mechanisms reminiscent of satisficing in both people and machines, and illustrate how BCSS offers a general approach for interpreting and comparing rule-based reasoning of humans and black-box AI systems.

NVIDIA Blog00:00

The Wizard of Open Weights: Chris Alexiuk Makes AI Easy With Open Models

Chris Alexiuk has a simple rule about open source AI: If you believe in it, be loud about it. “We should be shouting about this technology from the rooftops,” said Alexiuk, a Toronto-based senior product research engineer in deep learning at NVIDIA who specializes in making open large language models (LLMs) practical for working developers. […]

arXiv cs.AI13:00

Trust Responsive Runtime Authorization for Agentic AI with Empirical Characterization of Policy Behavior and Parameter Sensitivity

arXiv:2611.06996v1 Announce Type: new Abstract: The policy that converts runtime estimates of an autonomous agent's condition into operational authority is rarely specified or measured. We empirically characterize the Trust Responsive Authorization Policy (TRAP), which maps an Authorization Readiness Score (ARS), a weighted combination of runtime trust, operational stability, and contextual risk, onto discrete authorization levels through an asymmetric rule: escalation requires consecutive observations above a band edge plus a margin, while a sharp drop in readiness restricts authority at once. On one matched set of 100 Monte Carlo traces, we compare TRAP with static, trust-only, risk-only, and symmetric policies. Under abrupt degradation, TRAP restricts at onset and re-escalates sooner after recovery than the symmetric policy, with the highest and, descriptively, least variable stability among the adaptive policies, although more of its post-recovery escalations occur below the selected trust threshold. Paired bootstrap intervals exclude zero for the remaining differences. In one-factor sweeps, recovery time rises with the escalation margin and run length, shifts with the score weights, and is unchanged by the restriction threshold. In an exploratory post-freeze experiment, when the same degradation is spread over 5-20 intervals, TRAP restricts only on the 4 traces that retain a single-interval drop above that threshold; averaged over all 100 traces, it holds authority above what readiness supports for 15.9-18.9 of the 20 degradation intervals, against 0.8 under abrupt degradation and about 10 for the symmetric policy. Its restriction advantage therefore depends on degradation being abrupt. All reported values are traceable to the publicly available, versioned research release.

SiliconANGLE AI01:35

University of Utah retains operational sovereignty as Redtail expands research access

Operational sovereignty gives universities authority over how shared computing resources are allocated, managed and secured. The University of Utah’s Redtail AI factory supercomputer brings advanced computing capabilities to a statewide community. Supporting researchers, students, administrators and external partners through that infrastructure requires more than computing capacity, according to Manish Parashar (pictured), chief AI officer of […] The post University of Utah retains operational sovereignty as Redtail expands research access appeared first on SiliconANGLE.

InfoQ AI2026-11-06

The Evolution of Agent Harness: From Chains and Graphs to Code-Based Implementation

Click to view the original article>

2026-11-05

arXiv cs.DC13:00

A Controlled Experimental Evaluation of Configuration and Cache Architecture in LLM Serving Engines

arXiv:2611.05991v1 Announce Type: new Abstract: Multi-agent LLM applications repeat most of their prompt tokens across requests, and serving engines reuse the cached state of shared prefixes. vLLM and SGLang reuse it at different granularities, and benchmarks of their default deployments report large performance gaps, usually credited to the cache. Such comparisons rarely control for other configuration differences. One of them, whether a long prompt is prefilled in chunks, is set by each engine's defaults from the hardware and the release, and it confounds the comparison. We built a reproducible testbed on Kubernetes and a seeded multi-agent workload generator, and compared the two engines under vendor defaults and with chunked prefill matched. SGLang's configuration is the same in both arms, which makes it an internal control across independently provisioned deployments. Under defaults, vLLM's median time to first token is up to 64x SGLang's. Matching one setting removes 78% of the capacity gap where that gap is largest and 30 to 67% of the latency gap. Near saturation the remaining latency gap reflects queue growth. A fluid queue model driven only by a 4.4% throughput shortfall reproduces it, and shows that such a gap scales with the length of the benchmark run. What survives matching is a tail-latency advantage of 2.4 to 6.4x at equal throughput, consistent with cache-aware ordering of waiting requests. We derive six recommendations for comparing serving engines.

arXiv cs.AI13:00

A Feasibility Condition for Confidence-Gated Tiered LLM Inference

arXiv:2611.05394v1 Announce Type: new Abstract: Tiered inference distributes large language model computation across a hierarchy of devices, from on-device and edge hardware to the datacenter, and is increasingly offered as the route to cheaper, lower-latency, and more private serving. Most such systems orchestrate frozen model checkpoints, and most decide what to resolve locally and what to escalate using a confidence signal produced by a forward-deployed model. We give a closed-form condition for when no threshold on that signal makes a tiered cascade worthwhile relative to sending every query to the backstop. Modeling the routing decision as a binary test and separating local from escalation cost through an architecture cost ratio $\kappa$, we derive a feasibility condition bracketed by a signal-free floor, below which a cascade is feasible at any signal quality, and a perfect-signal ceiling, above which none is. Read along the $\kappa$ axis the condition inverts: where local compute is billed, cost binds, matching a recent empirical finding; where it is sunk, on decentralized hardware, feasibility becomes unconditional and signal quality instead sets what the cascade achieves. We turn the condition into three design requirements that existing trained-in primitives satisfy only in isolation, and we propose, as future work, a co-designed architecture and a protocol to test it. The condition and the inversion hold independently of that architecture; they are the paper's contribution.

arXiv cs.DC13:00

Ab Initio Accuracy Beyond the Cutoff: Distributed Message-Passing Equivariant Attention at Tens-of-Millions-of-Atom Scale

arXiv:2611.06122v1 Announce Type: new Abstract: Molecular dynamics predicts molecular motion from atomic forces and trajectory integration. Density functional theory (DFT) provides first-principles forces, but its $O(N^3)$ cost limits simulations to thousands of atoms; empirical force fields scale to millions but miss bond breaking and charge transfer. Equivariant message-passing Transformers learn the ab initio potential at linear cost. Stacked layers propagate information beyond a single cutoff, giving four layers a receptive field up to 23{\AA}. At this frontier, scale is limited by communication: layers exchange boundary features across devices, force backpropagation repeats the exchange, and moving atoms change neighbor graphs and tensor shapes. We present Tessera, a distributed runtime that fixes workload shapes between rebuilds. It sorts atoms along a space-filling curve into equal-count shards, ensuring identical atom counts and tensor shapes per rank. Peer sets scale as $K^{0.5}$, not $K$. A conservative neighbor skin preserves topology across updates, enabling each rebuild interval to replay as one CUDA Graph. Fused kernels, three-level neighbor search, and overlapped All-to-All complete it. On 512 GPUs, Tessera sustains 30.7M atoms at 89.7% weak-scaling efficiency (77--90% across three per-GPU loads); per-step time rises 1.11--1.29x as machine and problem grow 16x. Graph reuse cuts full-step time $1.87\times$. Across an $8\times$ device increase, neighbor-search cost grows $1.7\times$, versus $3.0\times$ for Morton order. In a live $10.24$M-atom salt solution, NVE energy drift is $0.42\,\mu$eV/atom/ps over $10$ ps, and structure matches a serial reference. A 10.06M-atom Norovirus capsid sustains 100 ps of NVT at $\approx 8$ ps/day on 256 A100 GPUs. Over 200,000 steps, it has no broken bonds or anomalously close pairs and uses three compiled variants, with no graph breaks or eager fallbacks.

Google Research02:07

Agile Deliberation: How AI helps humans define the gray areas

Data Mining & Modeling

arXiv cs.CV13:00

Augmentation with Reinforcement Transformation for Low-textured Scientific Sketch Image Retrieval

arXiv:2611.05408v1 Announce Type: new Abstract: Low-textured sketch images are widely used in scientific applications, including wildlife conservation, archaeological illustration, and forensic composites. Learning from and retrieving such images is challenging because they lack detailed surface textures and visual variations commonly exploited by modern deep learning models. This paper proposes Augmentation with Reinforcement Transformation (ART) to fine-tune pretrained models to learn effective representations for accurate sketch image retrieval. Our key observation is that sketches are particularly sensitive to image transformations, such as rotation, flipping, zooming, and stretching. While some transformations preserve their structural integrity, others may introduce distortions that hinder representation learning. Instead of randomly choosing transformations to construct fine-tuning views, ART formulates augmentation selection as a multi-armed bandit (MAB) problem, where transformations are treated as arms and their effectiveness is continuously evaluated through rewards. This enables ART to adaptively construct reward-guided view banks before self-supervised fine-tuning; the subsequent representation loss does not feed back to ART. On Florida manatee sketches provided by Mote Marine Laboratory, ART achieves 71.40% average Top-1 accuracy for transformed-scar retrieval, compared with 68.83% for random augmentation selection, and 55.84% for location-aware sketch retrieval, compared with 18.18% for frozen CLIP.

llama.cpp releases05:33

b12114

Fixed get_max_size to return maxBufferSize and improved the memset shader to handle buffer sizes exceeding the maximum binding size, preventing issues with whole-buffer bindings in memset and clear operations.

llama.cpp releases05:57

b12116

The llama.cpp release b12116 introduces support for the drama-3 text-to-speech model, includes cleanup improvements, and ensures the drama3 helper stops at the codebook tower limit. This update provides binaries for multiple platforms including macOS, Linux, Windows, Android, and iOS, with support for various backends such as CUDA, Vulkan, ROCm, OpenVINO, and SYCL. Some builds remain disabled or require additional setup.

llama.cpp releases06:14

b12118

The update enhances OpenCL performance for decode operations by optimizing workgroup usage for q4_K gate up and GLU, and adds support for Flash Attention (FA) in various configurations including GQA 2, 6, and 16 with different head sizes, improving efficiency on specific hardware setups.

llama.cpp releases11:00

b12121

A fix was implemented in llama.cpp to correct the calculation of on-device sequence state copy sizes for quantized key-value (KV) caches, ensuring accurate copying of data blocks and preventing crashes or incorrect state restoration. The update adjusts the element count computation using proper block size handling and removes redundant code elements.

llama.cpp releases14:15

b12122

The update introduces optimizations for CUDA by fusing NVFP4 gate/up operations and GLU within MMQ, honoring requested precision in fused gate/up MMQ, sharing scale checks for NVFP4, skipping unused FP4 kernels, testing with W4A8 configurations without scales and with batching, and adding checks for gate_scale before loading fused NVFP4 gate scale.

llama.cpp releases14:43

b12123

The update introduces DSV4_HC_PRE and DSV4_HC_POST support for OpenCL, along with improvements to dsv4_hc_post functionality, enhancing performance and compatibility across various platforms including macOS, Linux, Windows, Android, and embedded systems.

llama.cpp releases15:08

b12124

The release includes support for non-contiguous sigmoid operations in OpenCL, enhancing compatibility and performance across various hardware platforms. This update is part of a broader set of improvements and new binary distributions for multiple operating systems and backends, including macOS, Linux, Windows, Android, and openEuler, with support for CUDA, Vulkan, ROCm, OpenVINO, SYCL, and other accelerators.

llama.cpp releases15:29

b12125

The update enables f32 and q5_k multi-column GEMV by default for Adreno X2 on X2E devices, improving performance for specific computational tasks in the llama.cpp framework.

llama.cpp releases22:03

b12128

The fix addresses intermittent CI failures in TOPK_MOE tests caused by near ties in logits, particularly with uniform random inputs and narrow-range gating functions like sqrt_softplus. The solution involves redrawing logits until group selection and top-k decisions have a relative margin of 1e-4 in double precision, and constructing logits by inverting the gating function using a grid-based approach to ensure consistent expert selection across backends.

PyTorch Blog04:29

Call for Proposals: PyTorch Day China 2026

The Call for Proposals for PyTorch Day China 2026 closes November 10 at 23:59 China Standard Time. The event takes place in Beijing on December 12, bringing together PyTorch developers,... The post Call for Proposals: PyTorch Day China 2026 appeared first on PyTorch.