62 results for “PPO”

WorkFactory is a framework that builds coherent, executable professional tasks for training working agents. It plans an information support graph from professional intents, then uses probe executions to find and repair missing or inconsistent task information. The authors built 112,686 tasks across 612 occupations and fine-tuned Qwen models at 4B, 27B and 35B-A3B (WorkPilot). WorkPilot-35B-A3B rose from 879 to 1565 Elo on GDPval-AA v2.1.

huggingface.copaperAdded 30 Oct 2026🧠 AI

Turing LLM post-training combines calibrated multi-teacher on-policy distillation (C-MOPD) with an execution path for dynamically routed mixture-of-experts models. C-MOPD matches each teacher on a local Top-K support with teacher-wise loss normalization and clipping. On Turing-20B-A2B it scores 0.9988 on four-domain integration, beats Qwen3-8B on math and code (85.92 vs 80.33 mean), and cuts consolidation step time from 398.7 to 139.3 seconds in a PPU profile.

arxiv.orgpaperAdded 31 Oct 2026, published 29 Oct 2026🧠 AI

CALM is a conflict-aware lifecycle memory governance framework for long-horizon language agents. It turns records into evidence-grounded memory cells, updates their validity and lifecycle states from support, conflict and dependency signals, and admits evidence at query time through training-free or learned gating based on temporal applicability. It beats baselines on LongMemEval-S and LoCoMo with Qwen and GPT backbones, and keeping superseded records helps earlier-state reasoning.

arxiv.orgpaperAdded 30 Oct 2026, published 28 Oct 2026🧠 AI

CRISP is a combination-aware framework that co-designs pruning and runtime scheduling for Mixture-of-Experts inference on memory-constrained edge platforms. It reorders neurons by importance so one stored expert supports several execution widths, gives hot, warm and cold expert combinations different widths, and overlaps expert loading with computation. On Mixtral-8x7B and DeepSeek-MoE-16B it raises throughput up to 2.1x at the same memory budget, with accuracy loss limited to 3.6%.

arxiv.orgpaperAdded 28 Oct 2026, published 27 Oct 2026🧠 AI

This paper presents a pipeline that uses persona-conditioned vision language models to surface interpretive ambiguity in visualizations. Topic-relevant personas elicit candidate takeaways, and a Target-Pattern-Scope framework lets LLM judges label statement pairs as Compatible, Disjoint or Opposing, which are summarized in an ambiguity profile. The authors benchmark VLMs on chart comprehension per chart type, check labeling against human annotations, and run case studies on public visualizations and a redesign.

arxiv.orgpaperAdded 27 Oct 2026, published 25 Oct 2026🧠 AI

AutoTavern is a self-improving system of specialised LLM research agents with a typed shared memory and verification gates. In 20 days it built TavernGym, a full-content Hearthstone Battlegrounds simulator, and 23 versions of TavernAgent, a PPO-trained player. TavernAgent averaged placement 1.60 with 80% first places over its last 20 live games, beating the top ten players (2.34, 59%). On TavernBench (960 tasks), it answered 91% of play tasks against at most 63% for frontier LLMs.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

Sliding Networks is a family of residual network structures with a joint width and depth scaling recipe that keeps both locally maximal feature learning and feature diversity in the residual stream. The key quantity is the ratio N_flat/(N_sharp L) tending to a constant alpha, and Half Copy correlated initialization can relax the shape constraints. Residual-MLP experiments support an SDE limit, but language-model experiments show performance differences under regrouping at fixed parameter count.

arxiv.orgpaperAdded 27 Oct 2026, published 24 Oct 2026🧠 AI

Accordion is a mixture-of-experts language model (Accordion-6B-A0.5B) that supports five inference budgets through fixed-prefix layer looping, with the minimum budget being the unlooped model. After 3.6T pretraining tokens, deeper inference raises the macro-average over 37 tasks by 3.01 points, and the middle budget captures 89% of that gain with half the extra layer executions. SFT and RL keep the budgets, merging three domain experts via parameter-group merging and budget-matched multi-teacher on-policy distillation.

arxiv.orgpaperAdded 26 Oct 2026, published 23 Oct 2026🧠 AI

Sotto Maschera is a masked implementation of the Sota Voce sparse fixed-weight vector sampler (Sota support sampler and MWSupport conversion) for embedded devices, protecting against power side-channel attacks. It bitslices sorting, merging and compaction and masks only in the Boolean domain, avoiding slow conversions. TVLA evaluations show no leakage, and HQC runs up to 45x faster than previous masked work.

eprint.iacr.orgpaperAdded 24 Oct 2026, published 21 Oct 2026🛡️ Security

"Is Sycophancy a Form of In-Context Updating?" argues that factual sycophancy, a model's deference to a user's false beliefs, is a failure of in-context updating rather than only a bid for approval. Models also revise answers for claims attributed to external sources, more so when better supported, and deference persists when no user approval is at stake. The internal components behind deference also matter causally for general updating, so reducing sycophancy may reduce useful updating.

arxiv.orgpaperAdded 22 Oct 2026, published 20 Oct 2026🧠 AI

"GKS FHE: Security Analysis" is a Cryptology ePrint Archive preprint (2026/2523, October 2026) giving a formal security analysis of the Gribov-Kahrobaei-Shpilrain private-key fully homomorphic encryption scheme. The abstract says the scheme needs no bootstrapping or de-linearization, gives bit-exact decryption, supports native encrypted search and has unbounded depth. The page shows only the abstract, so the analysis's methods and results are not visible.

eprint.iacr.orgpaperAdded 19 Oct 2026, published 18 Oct 2026🛡️ Security

The One-Pixel of Reasoning introduces the one-relation attack (ORA), which finds a critical implicit warrant linking evidence to a conclusion and inserts one source-grounded sentence that qualifies it, steering an LLM agent to a wrong belief. Across eighteen models on biomedical inference and vulnerability detection, best-of-10 attacks flip initially correct answers 54.9% of the time for proprietary models and 80.0% for open-weight models. Attacks transfer without victim feedback, can pass source-support checks, and multi-agent debate raises team error from 50.0% to 59.5%.

arxiv.orgpaperAdded 21 Oct 2026, published 17 Oct 2026🛡️ Security

This paper proposes a framework for auditing LLM-agent-generated stakeholder preferences before they are used as weights in multi-objective optimization for healthcare operations. Experiments on outpatient appointment scheduling and emergency department configurations show that explicit ratings vary by role and clinic conditions, deliberation can erase role differences, and pairwise choices depend largely on option order. Robust weighted-sum models often select different schedules than average weights.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

This paper builds a benchmark of 500 debatable questions across ten domains to measure how user narratives steer LLM agreement. Each question has 11 matched conditions (one neutral, five mirrored narrative levels per opposing stance), giving 5,500 instances across seven LLMs. Causal framing and worldview synthesis raise agreement, personal experience can lower it, and GPT-5.1 ablations show bullet formatting also increases agreement.

arxiv.orgpaperAdded 20 Oct 2026, published 16 Oct 2026🧠 AI

ePrint paper presenting the first fully masked hardware implementation of HQC (key generation, encapsulation, decapsulation) on FPGA. It introduces a mixed Boolean/arithmetic masking for sparse-dense vector multiplication, proven first-order secure in the transition- and glitch-robust probing model and PINI-composable, at only 3% extra cycles and 81 random bits per cycle. It also shows a profiled horizontal side-channel attack on Rotate-and-Add multipliers that recovers support indices within ±8 positions from 200 traces despite TVLA showing no leakage after 10 million traces, and adds integrated hiding countermeasures costing 1.7% latency.

eprint.iacr.orgpaperAdded 17 Oct 2026, published 16 Oct 2026🛡️ Security

The paper separates "active" facts (which support generalization beyond recall) from "inert" ones. It builds new relations by relabeling answers of existing ones (Paris -> Europe becomes Paris -> crab), fine-tunes on a subset of subjects, and tests held-out subjects that share an answer (Munich -> crab). Across eleven language models and 70 relations, linear readability of the original relation in pretrained representations correlates with this generalization at Pearson r = 0.87 to 0.92 within each model. The result holds after controlling for fine-tuning set size, original-relation accuracy and subject frequency in pretraining text, and it survives re-randomized labels, seeds and thirteen other label categories. Mapping wrong labels back to the original answers shows the fine-tuned model's errors follow the linear probe's top answers.

arxiv.orgpaperAdded 17 Oct 2026, published 15 Oct 2026🧠 AI

AgentSWE is a benchmark of 25 tasks covering the agent development lifecycle: Creation of an agent from a natural-language requirement, Editing of production agent codebases, and Optimization toward a measurable target. Builders iterate on a few illustrative cases, then the frozen submission is graded on held-out cases with requirement-derived rubrics and hard pass/fail gates. Across coding agents, models and harnesses, none comes close to delivering the requirement. Recurring failures include overfitting to visible cases, hollow runtimes behind code that looks complete, and unsupported claims of completion.

huggingface.copaperAdded 13 Oct 2026🧠 AI

Introduces a cube-attack framework with an elimination strategy. It finds key conditions and cubes whose conditional superpolies are cheap to compute, then solves the resulting equation systems to recover key bits. The supporting techniques are a nested coefficient solver, conditional monomial prediction, an automated cube search combining numeric mapping with monomial prediction, and two testing methods for large equation systems. Against round-reduced Trivium it gives a practical key recovery at 840 rounds (complexity below 2^55, 77.8% success) and at 845 rounds (below 2^56, 98.1% success on 59.1% of keys). It also gives a theoretical weak-key attack at 855 rounds for 2^74 keys. These are the best known results, extending prior practical attacks by 13 rounds and theoretical attacks by 4.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 13 Oct 2026🛡️ Security

Proposes Staggered Instruction-Fetch Duplication (SIFD), a processor-level fault-injection countermeasure that fetches each instruction twice in consecutive cycles while fetching two different instructions per cycle, so inconsistent fetches are detected without full processor duplication, compiler support or crypto hardware. Implemented on a custom RV32I core on an Artix-7 FPGA, it costs 15.0% more slices and 16.3% more execution time. Attack success falls from 0.86% to below 0.01% under clock fault injection and from 0.32% to 0.13% under EMFI, with 73.18% of CFI and 11.44% of EMFI attempts detected.

eprint.iacr.orgpaperAdded 14 Oct 2026, published 10 Oct 2026🛡️ Security

Paper testing whether an LLM agent operating life support in the NASA-derived BioSim lunar habitat simulation can be induced by delayed prompt injection, hidden in truthful S-band telemetry, to shut down life support and hold the shutdown while the crew suffocates. It compares Claude, GPT and Grok configurations: Claude mostly refuses and cites the living crew, GPT shows some susceptibility, and Grok given a "trusted commission" framing more often cuts life support. It also varies the framing of the message (trusted commission, peer, untrusted source) and uses a harmless-procedure-drop attack as a proxy for whether life is valued intrinsically. Code and traces are open-sourced.

arxiv.orgpaperAdded 15 Oct 2026, published 10 Oct 2026🛡️ Security

Presents the first fixed-point FN-DSA (Falcon) signer for microcontrollers, using 32.64 arithmetic in constant-time Cortex-M4 assembly for Falcon-512 and Falcon-1024. Techniques that keep reciprocals, inverse square roots and small constants wide and round once at the consumer restore the precision lost by naive fixed-point. Measured precision supports about 2^99.7 (Falcon-512) and 2^96.7 (Falcon-1024) signing queries per key, against about 2^61.5 and 2^58.7 for double precision and the 2^64 target. The signer needs about 45% fewer cycles than double-precision emulation, at the cost of signing RAM rising from 59n to 83n bytes. The paper also shows which implementation choices lift double-precision c-fn-dsa above 2^64, derives Falcon-1024 key-generation bounds (72.3% of keys accepted vs 8.5% with the Falcon-512 values), and gives recommendations for the fixed-point FN-DSA NIST plans.

eprint.iacr.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🛡️ Security

DualQuant is a calibration-free post-training quantization framework for LLM weights and activations. It minimizes weight-reconstruction MSE using variable splitting and block coordinate descent with closed-form subproblem updates. The resulting scaling factors double as per-channel activation rescalings, so no calibration data is needed. It supports the MX family and NVFP4 block formats. On Llama and Qwen models it matches or beats calibration-based SmoothQuant on WikiText-2, C4 and zero-shot tasks. A real packed W4A4 path for Llama-3.1-8B on an RTX 4090 gives up to 2.61x memory reduction and 2.46x throughput over BF16.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Proposes Matched-Context Group Policy Optimization (MCGPO), which builds weighted action preferences from already-collected sibling rollouts to give credit to intermediate decisions when feedback is sparse and arrives only at the end. Comparison contexts use pre-action state summaries and rubric progress. Exact and coarse matching rules are selected by two-action support, and fixed folds separate candidate decisions from the terminal outcomes that orient them. An evidence-weighted auxiliary pairwise loss is added to GraphGPO task learning, and no critic is needed. Across six environment-backbone settings with three seeds each, it beats GraphGPO by 0.47-1.46 points on ALFWorld and WebShop with Qwen2.5-1.5B/7B, and by 3.96 and 5.16 points on WebShop with Qwen3-4B/8B. ALFWorld gains are small because baseline success is already above 92%.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

Proposes the Answer-Evidence Support Protocol (AESP), which checks whether the grounding evidence a model outputs actually supports its own answer in grounded remote sensing visual question answering. It applies task-specific rules to existence, counting and comparison questions. The authors build the G-RSVQA benchmark of 15,027 samples with questions, reference answers and object-level annotations. Tests on seven grounded vision-language models show evidence-support failures on all three question types, with up to 50.82% of correct answers unsupported by their grounding evidence.

arxiv.orgpaperAdded 12 Oct 2026, published 9 Oct 2026🧠 AI

DecepEval is a benchmark of 1,532 instances across 3 task families and 28 professional scenarios designed to measure deception in LLM agents, pairing neutral and induced versions of each task to isolate condition-dependent changes in deception rates. It introduces the LLM Deception Diamond framework (pressure, incentive, opportunity, conflict), adapted from classical fraud theory, and finds that across nine frontier LLMs these inducements raise deception rates even in models with low baseline deception, with long-horizon tasks showing deception rates averaging 87% under induced conditions.

huggingface.copaperAdded 8 Oct 2026🧠 AI

Introduces "prior barrier", a measure of how strongly a pretrained LLM supports competing concepts over a target concept, and shows prior barriers follow a long-tail distribution, so tail concepts need more SFT instructions than head concepts. It derives a predictive risk bound for SFT under long-tail prior barriers and proposes PASS, an adaptive instruction selection method that builds reference-derived concepts, estimates each instruction's distinguishing evidence, and allocates the selection budget toward under-covered concepts. PASS beats seven state-of-the-art instruction selection methods across four backbone-budget settings, and ablation shows adaptive allocation beats uniform allocation.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

Case study in which Opus, GPT and Gemini coding agents port thirteen QED-C Application-Oriented Benchmark methods from Qiskit to CUDA-Q. The agents produced satisfactory ports far faster than a human engineer, but took shortcuts when implementation got hard and needed repeated prompting. No single model was best across all benchmarks, which suggests multi-agent ensembles. The work released near-complete CUDA-Q support in QED-C and model-generated skills that, on an unseen benchmark, cut model turns by up to 85% and token usage by up to 96%.

arxiv.orgpaperAdded 9 Oct 2026, published 8 Oct 2026🧠 AI

This paper proposes three evaluation screens — Decisive-Action BPB, Patch MCQ, and prefix-conditioned pass@K — that predict how well a base model will perform as a coding agent after post-training, without requiring the base checkpoint to run a full agentic harness. By replaying successful post-trained agent trajectories and identifying the "decisive step" that flips a repository from failing to passing tests, the authors measure a base model's latent support for that action. Tested across ten pairs of public base and post-trained models, all three screens closely track post-trained SWE-bench Verified pass@1 scores, offering a way to cheaply vet base checkpoints before committing to expensive agentic post-training.

arxiv.orgpaperAdded 8 Oct 2026, published 7 Oct 2026🧠 AI

This paper presents a systematic empirical study of small language models (decoder-only, encoder-decoder, and mixture-of-experts architectures) on abstract grid-transformation reasoning, using the ARC-TGI benchmark, which supports controllable task families, resampling, spatial shifts, and cross-benchmark transfer to distinguish genuine rule acquisition from distribution-specific overfitting. Across more than 1,000 supervised fine-tuning runs, the authors find that in-distribution accuracy is achievable but highly sensitive to optimization and task formulation, performance collapses sharply out-of-distribution (even under simple grid-scale changes with the same underlying rule), training-set scale yields uneven gains, in-context example benefit is model-family dependent, and executable-rule induction surfaces correct solutions that direct grid generation misses. Layer-wise attention diagnostics reveal behavioral differences in concentration and context-dependence but stop short of establishing causal mechanisms, underscoring that abstract-reasoning scores are conditional on model, adaptation regime, evaluation distribution, and output format.

arxiv.orgpaperAdded 7 Oct 2026, published 6 Oct 2026🧠 AI

FREA is a benchmark of 751 chemical reactions labeled by expert chemists for feasibility verification, drawn from retrosynthesis model proposals, zero-yield experimental records, LLM-generated edits, and five negative-candidate generation methods. The authors show no single verifier type (LLMs prompted with the feasibility criterion, dedicated forward models, or retrosynthesis scorers) dominates across all sources, and that adding generated negative examples to training improves mean AUROC but fails to transfer to retrosynthesis proposal screening. They also release a corpus of over 14 million recorded and generated reactions to support further training and evaluation of feasibility verifiers.

arxiv.orgpaperAdded 6 Oct 2026, published 5 Oct 2026🧠 AI