SEARCH · Search arXiv

Search Search arXiv

Search indexed arXiv papers on artificial intelligence, large language models, computer vision and robotics. Read source abstracts and follow links to arXiv.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,693 records · Page 94Linked to original sources

arXiv2026

To Route or to Adapt? Multi-Expert Diffusion Planners in Unseen Regions

Diffusion trajectory planners with domain-specific experts face a practical question in unseen regions: can they rely on selecting among existing experts, or must the planner itself adapt? We study this question using a planner with a shared adapter and per-domain expert adapters. Across three real-world held-out maps, none of eight test-time routing schemes improves the fleet-mean lateral offset over the static default, which uses the same expert for every scene. Even an oracle that selects the best expert for each scene reduces the mean offset by only 0.9 m. In contrast, reward-based fine-tuning on just 20 target-region scenes yields substantially larger gains. On matched evaluation slices, joint replay achieves a fleet-mean offset of 6.9 m, compared with 12.4 m for the static default. Comparisons under the same target-region training budget show that target-region training drives the improvement, while replaying training scenes limits forgetting on the original ODDs. On nuPlan, fine-tuning across training cities also improves open-loop offsets, although closed-loop performance deteriorates. Further diagnostics reveal that two test-time adaptation baselines achieve low offsets by producing degenerate trajectories. These results show the limited headroom for selecting among existing experts and the greater open-loop gains available from adapting the planner.

cs.RO↗

arXiv2026

Co-Recursive Semantic Self-Extension: A Mathematical Foundation for Open-Ended Intelligence

Artificial intelligence has advanced through scale, data, and computation, yet remains bounded by its representational framework. We ask what makes intelligence open-ended. Every inferential system selects meaningful distinctions, limiting what it can learn. Within a fixed purpose, performance reaches a ceiling set by its semantic domain. Teleological succession, in which purposes arise from earlier ones, faces a similar limit: Boolean-closed purpose-transformations exhaust their reachable semantic territory. We shift from purposes to inferential systems. Drawing on Kant and Gödel, we model cognitive states as partial evaluations over a formal language. Unresolved propositions may be undecided or undecidable. Reasoning can resolve the former but not the latter, leaving a frontier for extension. Non-trivial reductions of undecidable propositions expose undecidable intermediate premises whose reflective reification supports multiple decisive extensions. Finite trace-guided generation remains fallible, requiring meta-level selection and polycontextural exploration. Using Kolmogorov complexity, we show that no architecture with a fixed state description, transition mechanism, and initial condition can generate histories incorporating irreducible algorithmic information. Because physical systems are fixed in this sense, we examine collectives. Coupling finitely many finite agents does not remove the bound. Extension requires persistent external memory preserving semantic content and historical coordinates. Collective generative capacity depends on balancing overlap for transfer with difference for generation. We propose semantic self-extension as a design principle: intelligence should not merely resolve ambiguity within existing meanings, but transform unresolved structures into new spaces of meaning, generating new distinctions, questions, representations, and purposes.

cs.AI↗

arXiv2026

RIFT: KV Cache Compression for Draft Models with Complementary Compressibility Training

Speculative decoding is a popular decode acceleration method. As such, it is particularly relevant to long-output generation, which requires an increasingly large KV cache. KV cache compression offers a way to constrain costs in long-context drafting. However, compression methods that focus on prefill information can lose relevance as the output length grows. We term this phenomenon decode staleness and show that it degrades draft model accuracy under selection-based KV cache compression. We introduce RIFT, a family of selection-based KV cache compression strategies that balance prefill information and recent generated tokens. We show that refreshing the KV cache selection during token generation significantly improves the drafter's quality at the cost of additional decode latency. To alleviate the refresh costs, we propose an adaptive KV refresh strategy that reduces the number of score calculations while retaining the drafter's quality. We further demonstrate how a compressibility-driven drafter training phase, based on sparse attention masks, can improve the drafter's quality under a compressed KV cache. Combined with compressibility training, RIFT enables Qwen3.5-4B, drafting for Qwen3.5-122B, to retain over 98% of its full-cache acceptance length on 32K-token inputs, 4K-token outputs, and an 8K-token KV cache budget.

cs.CL↗

arXiv2026

Sharp Variational estimates for Schrödinger equation in $\mathbb{R}^{2}$

For $p\geq2$ and $r\geq 1$, we prove the sharp strong $r$-variation estimate for solutions to the free Schrödinger equation $$ \bigl\|V^r_{[0,1]}\{e^{itΔ}f\}\bigr\|_{L^p(\mathbb R^2)} \lesssim \|f\|_{H^s(\mathbb R^2)}. $$ The estimate holds if $s> s(p,r)$ and fails if $s< s(p,r)$, where $$s(p,r)=\max\left\{ \frac{2}{r}+\frac{1}{p}-\frac12,\, \frac{2}{r}-\frac{2}{p},\, \frac{1}{p}+\frac{1}{r},\, \frac{2}{p}-\frac13,\, 1-\frac{2}{p} \right\}.$$ In particular, $s(3,\infty)=1/3$. Thus our result yields a sharp variational extension of the two-dimensional Schrödinger maximal estimate of Du, Guth, and Li [{Ann. Math.}, 186, 607--640 (2017)]. The proof employs fractal restriction estimates to eliminate the regularity loss originating from the summation in the variation norm.

math.CA↗

arXiv2026

Solving Flood-It Puzzles: A Comparison of Tailored A* Search with Declarative Solver Approaches

We present an optimized approach for finding minimal move sequences to solve Flood-It puzzles using A* search and compare it with approaches using SAT, Integer Programming, and Constraint Programming solvers. The performance of A* search is considerably improved by using an appropriate heuristic and sophisticated equivalences of game states to limit the search space. Each aspect is systematically evaluated, providing insights that generalize to other problems. By comparing the best variant of each approach, the A* search is the fastest in finding a minimal solution in about 75 % of the Flood-It instances tested. This result is in line with our expectation since A* specifically looks for a minimum solution. In contrast, the SAT-based approach is designed to solve the decision problem of whether a solution with a given length exists. An iterative search is required to determine a minimum solution length using a SAT-based approach. However, with increasing complexity, quantified by the minimum solution length, the SAT-based approach outperforms A*. We also compared the different approaches to the decision problem, where A* again performed best for the largest number of puzzles. Here, again, SAT supersedes A* for higher complexities, albeit with a lower crossing point.

cs.AI↗

arXiv2026

Saxion Decay and Dark Radiation in the KSVZ Axion Model

We calculate the relativistic axion energy density produced by the decay of a curvature-displaced Peccei--Quinn condensate in a quartic KSVZ model. We follow the radial PQ field (the saxion) as it relaxes from its inflationary minimum and determine the surviving condensate abundance from the adiabatic action of its oscillations. We then evolve the condensate through its decay into axions and visible particles to calculate the resulting dark-radiation contribution, expressed as $ΔN_{\rm eff}$. With one heavy coloured KSVZ fermion and no additional portal interactions included, the axion channel dominates the two tree-level decay modes. Two reference histories yield $ΔN_{\rm eff}\simeq0.033$ and $0.12$, with maximum saxion energy fractions below $1\%$ and $4\%$, respectively. At fixed quartic coupling, increasing the duration of curvature release reduces the predicted axion radiation density relative to visible radiation by a factor of approximately $28$ while preserving the primordial isocurvature amplitude.

hep-ph↗

arXiv2026

Nonexistence of Constant Congruences for the Cumulative Partition Function

Let $p(n)$ be the ordinary partition function and let $c(n)=\sum_{k=0}^{n-1}p(k)$, with $c(0)=0$. We prove that, for every $a\geq1$, $b\geq0$, and $m\geq2$, the sequence $c(an+b)$ is not eventually constant modulo $m$, thereby answering a question posed by Chen. The proof combines the algebraicity over $\mathbb C(j)$ of modular functions for finite-index subgroups of $\mathrm{SL}_2(\mathbb Z)$ with Voloch's theorem on the transcendence of elliptic modular functions in positive characteristic.

math.CO↗

arXiv2026

Interpreting the top quark mass using simulations of top-flavoured mesons

Active work is being done on the issue of the interpretation of the top quark mass in terms of well-defined theoretical definitions. This work considers this issue by simulating fictitious heavy-light top-flavoured mesons with a fixed mass in Pythia 8.3. Within Heavy Quark Effective Theory, the mass of the top mesons can be related to the pole mass of the top quark. By fitting final-state observables in top pair production at current hadron and future lepton colliders, we extract a negative mass difference between the input Pythia mass for the top quark and the pole mass of around $240$-$330$ MeV, consistent with $\mathcal{O}(Λ_{\rm QCD})$.

hep-ph↗

arXiv2026

Who Is the Influencer? Consensus Dynamics in Multi-Agent Systems

Multi-agent LLM systems are increasingly used in practice, but their behavior is complex and hard to understand. We design a simple task to ask which model has the most influence on the final consensus when several models are mixed in one system. Experimentally, across seven model families, we find that the opinions of the other models are absorbed into the opinion of the stronger model, the one with more parameters. This happens even though the task requires no pretraining knowledge and has no correct answer. To understand this phenomenon, we interpret the agents as weighted particles moving on a simplex. Larger models carry a larger weight and therefore occupy a deeper well in the energy landscape. A large weight means strong in-context learning: a model with a large weight amplifies the majority of its context. Larger models are generally better at in-context learning, and this is what makes the larger model the influencer. We hope that this work helps to interpret multi-agent systems, whose behavior is becoming too complex for humans to understand.

cs.AI↗

arXiv2026

Testing for peer effects in short panels without identifying the network structure

We propose an instrument-based test for peer effects in short-T panels when the underlying network is unknown. The test avoids estimating the network by aggregating pairwise comparisons between covariate-projected outcomes of potential peers and residualized own outcomes. The statistic is designed for fixed-T, large-N settings with individual fixed effects, heterogeneous slopes, heteroskedasticity, and serial dependence within units. We also develop a weighted extension that accounts for additive common time effects. The limiting distribution is standard Gaussian when the norm of the projected component of the outcomes is bounded away from zero, but can be nonstandard when it is local to zero. We derive a conditional Gaussian limit experiment and a feasible conditional Monte Carlo procedure to obtain uniformly valid critical values. Monte Carlo evidence shows that the conditional procedure leads to accurate size control while retaining power against latent peer-effect alternatives.

econ.EM↗

arXiv2026

Learning What to Ask for Tomorrow: Active Preference Acquisition for Long-Term Personalized Web Agents

Web agents increasingly serve the same user across repeated online tasks such as booking travel, shopping, and scheduling, so their long-term usefulness depends on how well they know the user's preferences. Passive memory and personalization methods cannot obtain preferences the user never reveals, and clarification methods that value a question by its benefit to the current task overlook the benefit of reusing the answer in later tasks. We study Active Preference Acquisition (APA): which missing preferences are worth the cost of asking? We define the Prospective Personalization Value (PPV) of a missing preference as its expected cumulative utility gain over future tasks minus the effort and disclosure-risk cost of asking, and select questions under a per-interaction budget. We characterize when cumulative reuse justifies acquisition despite insufficient single-task benefit, and evaluate this regime with an initial-profile diagnostic. APA computes the counterfactual gain of each candidate preference on public task prototypes with the agent's own task solver and aggregates it by the expected reuse of each task family. We evaluate it on ActiveWebPref, a controlled longitudinal benchmark with hidden but queryable user profiles and exact task utilities. At a horizon of 20 tasks, 34.4% of budget-feasible valuable missing preferences are amortization-dependent; APA acquires 80.3% of them, whereas current-task value of information acquires 5.1%. Under per-user asking-cost caps matched to APA's realized expenditure, APA achieves higher net utility than four heuristic selection baselines. In our cost setting, asking about every unknown preference yields lower net utility than not asking, while APA recovers 84.2% of its utility gain with 27.6% of its questions. The value of acquiring a user preference should account for its reuse across future tasks, not only its benefit to the task at hand.

cs.CL↗

arXiv2026

DRAFT: Decoding and Reconstructing 3D Room Layouts from fMRI and Camera Trajectories

While functional magnetic resonance imaging (fMRI) decoders reconstruct images and 3D objects, recovering the metric geometry of rooms from a moving viewpoint remains an open challenge. We introduce, to our knowledge for the first time, the task of reconstructing 3D layouts of held-out scenes using only the known camera trajectories and the fMRI signals of participants watching indoor walkthroughs, without any visual frames. The task is hard because of the temporal mismatch between rapid visual changes and blurred fMRI signals. Our method, DRAFT (Decoding and Reconstructing 3D room lAyouts from fMRI and Trajectories), extracts a compact layout representation from the early visual cortex and fuses this neural code with the geometric free space derived from camera motion. To evaluate it, we propose layout r, the Pearson correlation between predicted and ray-cast log depths. On ten held-out scenes (20 participants), DRAFT reaches a layout r of 0.480, outperforming the camera path alone (0.446) and seven fMRI decoders (at most 0.429). DRAFT also works as a plug-in that improves all seven decoders without retraining, raising their layout r from 0.171-0.429 to 0.487-0.493. Brain activity and camera motion can thus recover the metric layout of unseen rooms, a first step toward reconstructing the 3D spaces people see.

cs.CV↗

arXiv2026

Zero Forcing, Support Varieties, and Boundary Complexes

Let a finite simple graph be given. We study symmetric matrices with this graph pattern that admit a null vector with prescribed exact support. Such a vector exists precisely when its support is a fort, meaning that no vertex outside the support has exactly one neighbor inside it. Over the complex numbers, we prove that the Zariski closure of each exact-support coefficient locus is irreducible and compute its codimension in terms of boundary vertices and a labeled forest on the connected components of the support. The corresponding forest deficiency also determines the generic restricted nullity. An incidence parametrization yields a prime decomposition of the reduced zero-forcing failure locus. For a prescribed common kernel, we describe the coefficient space through equilibrium stresses, exterior affine dependencies, and free coefficients. We then construct boundary complexes recording local solution values and equation residuals. Their gluing maps are defined over the universal coefficient ring and commute with arbitrary base change. By retaining the complete collection of support signatures associated with each seed set, we obtain a composition formula for the minimum zero-forcing number and an algorithm for its computation.

math.CO↗

arXiv2026

On Modified Newton Methods for Unconstrained Optimization Based on Lanczos Tridiagonalization

For a strongly convex unconstrained minimization problem, Newton's method with a line search offers rapid convergence to a minimizer by incorporating second-order information. However, for a general unconstrained problem the Hessian need not be positive definite, which can lead to the inability to find a search direction or to search directions that fail to produce descent. Modified Newton methods are designed to handle this situation. This paper considers modifications to a Newton-conjugate-gradient approach based on Lanczos tridiagonalization. Existing methods are extended using dynamic modification schemes that provide a descent direction while keeping the modification bounded. A Lanczos-based scheme and a conjugate-gradient (CG)-based scheme are formulated and analyzed. These methods are mathematically equivalent in exact arithmetic, but their practical behavior differs in finite precision. Finally, it is shown how the convergence and computational cost are affected by varying the amount of additional curvature introduced and by allowing the early termination of the inner iterations.

math.OC↗

arXiv2026

Where Do Long Captions Fail? Position-Aware Diagnosis and Reinforcement Learning for Detailed Image Captioning

Detailed image captioning is usually framed as an amount problem: a model should mention more visual facts while avoiding hallucination. However, autoregressive VLMs generate captions as ordered sequences, and later generations may be produced under weaker or noisier visual grounding. We therefore argue that long-form captioning should be studied not only by what facts are mentioned, but also by where they appear. We introduce a position-aware rubric representation that decomposes dense human references into atomic visual claims, verifies generated captions against those claims, and induces a quartile-wise composition of supported, hallucinated, and generic content. This lens reveals two qualitatively different failures in long captions: models tend to produce unsupported specificity in the middle, while the final portion often escapes into vague, non-evidential language rather than simply accumulating more hallucination. A same-reference human control does not exhibit these patterns; additional human audits examine the visual validity of the induced labels. Motivated by this diagnosis, we propose PAGER (Position-Aware Grounded Evidence Reinforcement), a GRPO training objective with separate signals for supported fine-grained middle evidence, evidence-bearing tail continuation, claim coverage, and content-aware length control. The result is a closed loop: the same ordered claim representation identifies where long captions fail and supplies the position-specific reward needed to repair those failures. Experiments on detailed-caption benchmarks show that PAGER improves position-wise grounding and pairwise caption preference while preserving competitive global caption quality.

cs.CV↗

arXiv2026

Irrationality Exponents of Real CM Elliptic Periods and Powers of the Lemniscate Period

We give a determinant proof that every nonzero real period of a nonsingular elliptic curve over $\mathbb Q$ with complex multiplication, measured with a specified nonzero rational invariant differential, has irrationality exponent two. The proof uses an integral origin chart, matched local lengths, an actual compactification and weighted packet map, and arithmetic and analytic estimates for the same selected minor on a rectangular sublattice of the full CM period lattice. For the integral-defined lemniscate period $Ω=2\int_0^1(1-t^4)^{-1/2}\,dt$, we also give the uniform bound $μ(Ω^n)=O(n\log(2n))$ for integers $n\geq1$. Its new arithmetic ingredient clears integration denominators prime by prime while retaining the coefficient order; a one-root norm argument and an analytic estimate at algebraic centers complete the power proof. The two arguments are presented with their different charts and exact dependencies. Lean verifies the new CM determinant argument and rational normalization relative to explicit classical uniformization, CM-lifting and theta inputs; the lemniscate-power endpoint is also verified. The formalization record states the exact classical boundary and theorem references.

math.NT↗

arXiv2026

Can AI Co-Design Structures? An AI Agent for Early-Stage Structural Design

Recent advances in large language models and agentic workflows offer a new paradigm to early-stage structural design translating design intent into structural models. Although text-to-3D approaches have demonstrated that natural language can be used to produce visual, mesh-based geometric models, structural design requires domain-specific geometric arrangements and semantically rich data types. This paper introduces a proof-of-concept workflow for prompt-to-structure generation using an AI agent connected to a COMPAS-based structural modelling API through a Model Context Protocol (MCP) server. Through controlled modelling operations, the agent creates and modifies a graph-based structural data model composed of explicit building elements. The resulting artifacts can be inspected, modified, serialised, and further connected to downstream AEC workflows. The approach is demonstrated through selected prompt-to-structure examples and by progressive and editable design sequences that resemble typical iterative structural design workflows. The results are preliminary, but demonstrate the possibility of combining natural language, agentic workflows, and domain-specific structural data structures as a first step toward AI-assisted structural co-design.

cs.AI↗

arXiv2026

What Comes Next and Why: Interpretable Next-Event Prediction with Neuro-Symbolic Rules

In real-world applications, being able to explain why a prediction is made is often as important - if not more so - than achieving high accuracy. However, existing methods for sequential event prediction typically prioritize accuracy at the expense of interpretability. Neuro-symbolic approaches offer a promising direction to improve interpretability by learning symbolic rules, but extending them to next-event prediction is challenging due to the presence of temporal dependencies. We introduce RULES, a neuro-symbolic model that learns sequential rules directly from data, and combine it with a residual network to form RUNES (Rule Network for Event Sequences). RULES accurately recovers ground truth rules from synthetic data and discovers compact, interpretable rule sets on real data. RUNES combines these rules additively with a residual network, achieving competitive accuracy while providing transparent access to active rules and how residuals adjust predictions.

cs.LG↗