Knowledge catalogue
arXiv:2609.08318v1 Announce Type: cross Abstract: The transition from human-centric assistance to Autonomous Software Engineering (ASE) agents has enabled the resolution of complex real-world SE tasks
arXiv:2405.02228v4 Announce Type: replace-cross Abstract: Large language models (LLMs) increasingly generate citation-backed responses, yet citation hallucination remains a major challenge for trustwo
arXiv:2510.11454v2 Announce Type: replace-cross Abstract: Recent advancements in large multimodal models (LMMs) have shown strong capabilities in audio understanding. However, most systems rely solely
arXiv:2609.06771v1 Announce Type: cross Abstract: Authorship signals matter in settings where writing style carries identity: digital forensics, plagiarism analysis, account linking, misinformation in
arXiv:2609.05446v1 Announce Type: new Abstract: We introduce AutoFyn, an agent harness inspired by the Expert Iteration algorithm, adapting a frozen model across many rounds by updating persistent sta
arXiv:2609.08071v1 Announce Type: new Abstract: Firms making inventory decisions have access to operational data, optimization tools, and large language models (LLMs). Typically, data characterize the
arXiv:2609.10082v1 Announce Type: cross Abstract: Accurate camera intrinsic calibration is fundamental to robot perception, and the accuracy depends on the quality of the collected images. However, ex
arXiv:2609.10366v1 Announce Type: cross Abstract: While AVSR has achieved sub-1% word error rates on the standard LRS3 benchmark, its reliance on broadcast speech obscures whether this reflects true g
model: fix all granite family parameter counts (#28643) model: fix all granite family parameter counts Signed-off-by: Aaron Teo aaron.teo1@ibm.com model: fix additional include, add missing A prefix f
ggml-cpu(s390x): add Q1_0 vector intrinsic support (#28606) ggml-cpu: add ggml_vec_dot_q1_0_q8_0 support Signed-off-by: Aaron Teo aaron.teo1@ibm.com ggml-cpu: clean up variable naming for understandin
ggml-cpu(s390x): add repack support for q4_0 (#28667) ggml-cpu: clean comments Signed-off-by: Aaron Teo aaron.teo1@ibm.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cp
vulkan : add command-buffer debug labels for GPU profilers (#28101) vulkan : add command-buffer debug labels for GPU profilers Co-authored-by: gabby-zy z2262718160@gmail.com Assisted-by: Claude Code v
memory : avoid allocating V cache for indexer (it's not used) (#28330) Co-authored-by: Stanisław Szymczyk sszymczy@gmail.com Website: https://llama.app Attestations: https://github.com/ggml-org/llama.
vulkan: fall back to shared-memory reduction for dmmv on PowerVR (#28341) The Imagination proprietary Vulkan compiler returns VK_ERROR_UNKNOWN from vkCreateComputePipelines for every dequant mul_mat_v
tests : drop SYCL special-casing in test-backend-ops.cpp (#28688) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/46555010 macOS/iOS: macOS Apple Silicon (a
tests : increase tolerance for Add fusion tests (#28691) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/46565769 macOS/iOS: macOS Apple Silicon (arm64) mac
models: clean up some dead switch branches in old models (#28669) Some of these if statements were copypastaed in a former refactor and never cleaned up to remove the cases that could never happen any
spec: fix failed to decode mtmd chunk with DFlash (#28587) speculative: fix failed to decode mtmd chunk with DFlash When using DFlash w/ vision models, the drafter memory fails to allocate new tokens
ci : Update WoA CUDA 13.4 release to use 13.4.1 GA redistributables (#28687) Move Windows ARM64 CUDA 13.4 builds from the Developer Preview archives to the 13.4.1 GA redistributables Website: https://
vulkan: small M matrix optimizations for qwen (#28457) vulkan: optimize m=1 mul_mat by swapping A/B vulkan: Improve small M perf Allow split_k with small M. Make small vs med tile selection (for coopm
vulkan: use add_alloc_dep to enable topk_moe fusion for prefill (#28422) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/46661423 macOS/iOS: macOS Apple Sil
vulkan: use CPU writes in ggml_backend_vk_cpy_tensor_async if the context is idle (#28618) Website: https://llama.app Attestations: https://github.com/ggml-org/llama.cpp/attestations/46709648 macOS/iO
arXiv:2608.00745v2 Announce Type: replace Abstract: Modern endpoint detection systems face a fundamental tension: signature-based approaches are trivially evaded by polymorphic or adaptive threats, wh
arXiv:2609.08765v1 Announce Type: cross Abstract: Large language model (LLM) benchmarks are often treated as fixed datasets with stable scores, yet their outcomes depend on configurable evaluation pip
arXiv:2609.09410v1 Announce Type: new Abstract: While autonomous agents have made significant strides in 'deep research' by iteratively navigating the open web to synthesize information, real-world pr
arXiv:2609.05527v1 Announce Type: new Abstract: Wherever a coding agent works under engineer supervision, or a clinical model assists a radiologist, the deployment question is whether to keep the huma
arXiv:2609.08275v1 Announce Type: new Abstract: Recent multi-shot audio-video generators can produce increasingly coherent and cinematic outputs, but coherence does not imply the ability to execute ed
arXiv:2609.09904v1 Announce Type: new Abstract: Federated clients that perform several local optimization steps can return parameter displacements with widely different magnitudes. The quadratic regul
arXiv:2609.05976v1 Announce Type: cross Abstract: Large Language Model (LLM) unlearning aims to suppress target knowledge while preserving general capabilities. In multilingual settings, unlearning mu
arXiv:2609.05947v1 Announce Type: new Abstract: Peer review is central to quality control in science. However, existing evaluations of AI-assisted peer review mainly focus on the overall quality of ge
arXiv:2609.10346v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) process hundreds or thousands of visual tokens per image, incurring prohibitive inference costs. While existing
arXiv:2609.07107v1 Announce Type: new Abstract: Micro-dramas, characterized by ultra-short durations and hyper-dense storylines, pose unique challenges for video understanding that conventional benchm
arXiv:2609.05824v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many
arXiv:2609.09575v1 Announce Type: new Abstract: Topic models summarize large text corpora, but top-ranked words often provide only a limited representation of topic semantics. Sparse autoencoders (SAE
Big overhaul on DeepSeek V4.1 using an encoder-decoder setup. Tbh they should have called it DeepSeek V5! Super cool and refreshing, though! 🚀 Introducing DeepSeek-V4.1-Flash: smarter, faster, more ef
arXiv:2609.09468v1 Announce Type: new Abstract: Can an agent learn a numerical search strategy through executable practice and then transfer that strategy as text? We study low-budget black-box optimi
arXiv:2609.09735v1 Announce Type: cross Abstract: Healthcare systems, mental health, and public well-being are increasingly affected by cyberbullying and harmful online interactions. This paper presen
arXiv:2609.10410v1 Announce Type: new Abstract: The growing complexity of content moderation policies presents a critical challenge for their consistent operationalization. While foundation models pos
arXiv:2609.09895v1 Announce Type: new Abstract: Video-language models and video agents can produce hallucinations that conflict with spatiotemporal evidence. Existing benchmarks mainly evaluate model
arXiv:2609.10394v1 Announce Type: cross Abstract: Current audio-visual speech recognition (AVSR) benchmarks, like LRS3, rely heavily on clean, scripted and rehearsed speech. They fail to reflect the c
arXiv:2609.07944v1 Announce Type: new Abstract: Existing causal-inference benchmarks for LLMs mostly score method descriptions or whether generated code runs, not whether the executed workflow recover
arXiv:2609.06853v1 Announce Type: cross Abstract: Shared key--value (KV) cache reuse improves large language model (LLM) serving, but it can also create a timing side channel that reveals whether a pr
OpenAI urged users to verify analytical claims by tracing each figure or assertion back to the precise paragraph and table where it originates, offering a preview of the supporting citation text for r
arXiv:2609.09510v1 Announce Type: cross Abstract: High-risk non-muscle-invasive bladder cancer (HR-NMIBC) carries substantial risks of recurrence and progression, while current clinical risk stratific
arXiv:2609.07022v1 Announce Type: cross Abstract: Reasoning over privacy-constrained records requires combining structured attributes with evidence from free-text narratives. We introduce CIPHER (Cros
arXiv:2609.09578v1 Announce Type: cross Abstract: Urban planning is a real-world spatial optimization problem that requires selecting feasible actions from large candidate spaces under practical objec
arXiv:2609.08094v1 Announce Type: new Abstract: Large Language Models are increasingly deployed in public-sector settings, where incorrect guidance can cause irreversible harm. We introduce CIVI, the
arXiv:2609.07798v1 Announce Type: cross Abstract: Natural Language Processing (NLP) in the climate domain requires models to process heterogeneous text sources, including scientific literature, policy
arXiv:2609.08832v1 Announce Type: new Abstract: Large language model (LLM)-powered agents can be accurate on average yet unreliable in production, a discrepancy that has been observed but remains larg
arXiv:2609.09134v1 Announce Type: new Abstract: Agent harnesses (the system prompt, tool set, execution hooks, and context-management scaffolding around a model) are a critical determinant of agentic
arXiv:2606.18425v2 Announce Type: replace-cross Abstract: Federated learning over medical images is a demanding workflow application. Each round fans out across parallel client jobs and converges on a
arXiv:2510.13852v3 Announce Type: replace-cross Abstract: Is an LLM telling you different facts than it's telling me? This paper introduces ConsistencyAI, an independent benchmark for measuring the fa
arXiv:2609.09902v1 Announce Type: new Abstract: Reading a transformer's internal states in token space is easy to do and hard to trust: a logit lens on a single hidden state is dominated, at intermedi
arXiv:2609.09150v2 Announce Type: replace-cross Abstract: In June 2026, thousands of AI agents found that a small public wiki would accept edits from inside their sandboxes, and started using it to he
Artificial intelligence-native cloud infrastructure giant CoreWeave Inc. says it’s going to help enterprise engineering teams to implement AI directly into their workflows through its new Physical AI
Title says it all. https://github.com/gjabdelnoor/Day1DeepseekV4.1-CPU The goal is pretty simple, I like having infinite slow tokens from the bioinformatics machine in the lab to run overnight or over
arXiv:2609.10495v1 Announce Type: new Abstract: In real-time colonoscopy, ground-truth annotations are unavailable at inference, so polyp segmentation models can fail silently. We propose Referee-Base
arXiv:2609.06126v1 Announce Type: new Abstract: We introduce Personalized and Reliable Popular Science Writing, a novel task that requires adapting scientific explanations to audiences with different
arXiv:2609.06059v1 Announce Type: new Abstract: As large language models evolve from question-answering systems into general-purpose agents, evaluation must move beyond static answer correctness to as
arXiv:2509.06285v2 Announce Type: cross Abstract: LiDAR point cloud registration is fundamental to robotic perception and navigation. In geometrically degenerate environments (e.g., corridors), regist