神经程序综合

In this work, we focus on extending SHARP, the popular photorealistic view synthesis method, for universal monocular rendering across diverse camera systems, such as large-field-of-view fisheye lenses. To overcome the pinhole-specific assumptions of SHARP, our key idea is to align various images in a unified omnidirectional latent space. Thus, we propose UniSHARP, which performs implicit alignment in both feature and Gaussian spaces. Specifically, 2D semantic embeddings and 3D spatial features are jointly encoded and decoded by UniK3D to support Gaussian construction, while a ray-based universal representation organizes Gaussian primitives along rays and radial distances. To comprehensively evaluate our method, we construct a benchmark covering diverse imaging systems, including pinhole, fisheye and panoramic cameras, across various scenes. The benchmark is further stratified by field of view, spanning narrow perspective, wide-FoV, fisheye, and full panoramic settings. Extensive experiments on the proposed benchmark demonstrate the effectiveness of UniSHARP, outperforming alternative methods by a large margin. Our models, training code, and dataset will be publicly available.

We present StereoSplat, a feed-forward 3D Gaussian Splatting architecture designed explicitly for stereo videos to achieve reliable, metric-scale novel-view synthesis. Unlike monocular approaches that neglect the nature of binocular pairs, StereoSplat adopts a native stereo architecture that extracts dense multi-scale latent features and disparity from a foundation stereo backbone and feeds them into a dedicated Gaussian decoder. Our decoder utilizes cross-view feature fusion and gated spatial refinement to directly translate these rich representations into per-pixel 3D Gaussians. To resolve the high redundancy of per-pixel predictions across overlapping views, we propose a learnable depth-adaptive aggregation mechanism that clusters and aggregates primitives in inverse-depth space while preserving fine-grained details. To support robust training and generalization, we introduce XRStereo, a large-scale synthetic dataset of stereo video featuring various rig configurations tailored to match the physical hardware of real-world platforms. Trained exclusively on synthetic data and evaluated across challenging benchmarks, StereoSplat yields substantial improvements in the depth accuracy of rendered novel views over established baselines while maintaining high-fidelity novel view synthesis. By overcoming prior geometric limitations, our framework enables robust zero-shot transfer to real-world domains, offering a scalable geometric foundation for spatial computing in mixed reality, autonomous driving, and robotics.

LLMs have shown impressive success in program synthesis, discovering programs that surpass previously known solutions. However, these approaches rely on simple numeric scores to signal program quality, such as the value of the solution or the number of passed tests. Because such a score offers no guidance on *why* a program failed, the system must generate and evaluate many candidates per problem in the hope that some succeed, increasing both LLM inference and evaluation costs. We study a different approach: property-guided LLM program synthesis. Instead of scoring programs after evaluation, we check whether a candidate satisfies a formally defined property. When the property is violated, we stop the evaluation early and provide the LLM with a concrete counterexample showing exactly how the program failed. This feedback drastically reduces both the number of program generations and the evaluation cost, and can guide the LLM to generate stronger programs. We evaluate this approach on PDDL planning domains, asking the LLM to synthesize *direct* heuristic functions: every state reachable by strictly improving transitions has a strictly improving successor. A heuristic with this property leads hill-climbing search directly to a goal state. A counterexample-guided repair loop generates one candidate program, checks the property over a training set, and returns the first case that violates the property. We evaluate our approach on ten planning domains with an out-of-distribution test set. The synthesized heuristics are effectively \emph{direct} on virtually all test tasks, and compared to the best prior generation method our approach generates seven times fewer programs per domain on average, solves more tasks without using search, and requires several orders of magnitude less computation to evaluate candidates. Whenever a problem admits a verifiable property, property-guided LLM program synthesis can reduce synthesis and evaluation cost while improving program quality.

Many everyday programming tasks resist clean rule-based implementation, such as alerting on important log lines, repairing malformed JSON, or ranking search results by intent, and are increasingly outsourced to large language model APIs at the cost of locality, reproducibility, and price. We propose *fuzzy-function programming*: compiling such a function from a natural-language specification into a compact, locally-executable neural artifact. We instantiate this paradigm with Program-as-Weights (PAW), in which a 4B compiler trained on FuzzyBench, a 10M-example dataset we release, emits parameter-efficient adapters for a frozen, lightweight interpreter. A 0.6B Qwen3 interpreter executing PAW programs matches the performance of direct prompting of Qwen3-32B, while using roughly one fiftieth of the inference memory and running at 30 tokens/s on a MacBook M3. PAW reframes the foundation model from a per-input *problem solver* into a *tool builder*: invoked once per function definition, it produces a small reusable artifact whose subsequent calls per function application are cheap and offline.

Modern text-to-image models produce high-fidelity images but still struggle with compositional prompts that require instance identity, attribute ownership, counting, spatial ordering, and role-sensitive relations. We introduce *Panoptic Scene Program Diffusion Transformer* (PSP-DiT), a diffusion-transformer architecture that treats a panoptic scene program as a first-class latent variable rather than an external control signal or post-hoc parse. PSP-DiT jointly denoises image latents and scene-program latents through coupled transformer streams, while panoptic grounding and cycle-consistency objectives tie object instances, attributes, relations, and counts to visual support in the generated image. Under matched training and inference settings, PSP-DiT improves over a strong flat-text baseline across GenEval 2, SANEval-Simple, PSG-Score, and DetailMaster, with the largest gains on counting, attribute binding, role-sensitive relations, and long structured prompts. The method preserves image quality, adds modest inference overhead, and remains robust to imperfect scene programs.

Reactive synthesis from temporal logic modulo theories (LTLt) automatically con- structs correct-by-construction controllers for systems with arithmetic constraints— elevators, thermostats, robotic patrols—but the state-of-the-art CEGRES algorithm times out on 77% of existing benchmarks due to a fundamental bottleneck: it discovers the universally valid theory tautologies needed to refine its Boolean abstraction one at a time, through expensive counterexample analysis. We present NEUROSYNTHEOS, a neural augmentation of the CEGRES loop that predicts batches of tautologies at each refinement step. We formalize template invariance for parametric specification families, showing that the prediction problem reduces to constant re-instantiation when specifications share algebraic structure. We train a GNN over formula abstract syntax trees and an autoregressive transformer on tautology traces from 104 solved benchmarks. NEUROSYNTHEOS establishes a new state of the art for LTLt synthesis: on 83 standard benchmarks, it solves 60 ±1.2 specifications (vs. 19 for the previous best), resolving 41 that no prior method could handle within a 5-minute budget, while reducing the median itera- tion count by 5.2×with under 3 seconds of neural overhead. All predictions are SMT-verified before injection, preserving full soundness guarantees.

Reasoning, planning, and agentic systems demand that neural models extrapolate from the easy training instances to harder, more compositional problem instances they encounter at deployment. Neural networks trained by gradient descent are reliable interpolators \emph{within} their training domain but struggle to extrapolate beyond it: they learn local generalizers even when global solutions are representable in their architecture class. We propose a \emph{learn locally, recurse globally} paradigm-: train a neural model on in-distribution sub-problems and apply it iteratively under a verifiable outer loop that decomposes hard instances into a sequence of in-distribution sub-instances and reassembles the result. We instantiate this paradigm in Boolean circuit synthesis from truth-tables, which we adopt as a faithful, controllable abstraction of logical reasoning: any propositional inference reduces to evaluating a Boolean circuit, and circuit \emph{depth} is a clean proxy for the difficulty of the underlying reasoning task. Empirically, a $2.2$M-parameter encoder-decoder Transformer trained on depth-$\leq 2$ circuits, wrapped in our outer loops, synthesizes circuits at $5\times$ training depth and at input arities for which the base model's encoder is too small for direct inference, with synthesis quality matching a standard EDA tool while producing markedly shallower circuits. We connect the algorithms to classical Boolean decomposition theory (Shannon, Ashenhurst-Curtis, Reed-Muller) and to recent formal results on the AC$^0$ barrier for constant-depth Transformers.

AI agents increasingly excel at generating, testing, and refining code. However, they fall short on tasks requiring formal guarantees of full coverage that testing alone cannot provide. Distributed systems are a prime example: properties such as consistency between reads and writes must hold under every possible interleaving of events. Mechanized formal verification can guarantee such correctness, but typically demands months to years of expert effort. As evidence, even SOTA coding agents (Claude Code with Opus 4.6, Codex with GPT-5.4) succeed on only 2/7 distributed key-value-store specifications. In this paper, we present the first effective approach to addressing this gap, Inductive Deductive Synthesis (IDS), which jointly and incrementally synthesizes implementation and proof, and learns from failed attempts to systematically try promising strategies. Built as an agentic LLM system, IDS achieves 7/7 in about 6.8 hours and $106 per spec on average, roughly 200× faster than expert effort and 17% cheaper than SOTA agents. IDS further incorporates performance feedback into the same loop, yielding implementations up to 3× faster than published verified systems.

Synthesizing Mixed-Boolean Arithmetic (MBA) expressions from input-output examples is central to program deobfuscation and also useful for compiler optimization, reverse engineering, and cryptanalysis. Existing MBA synthesizers are typically CPU-based and scale poorly on large specifications or complex targets. Recent GPU-accelerated synthesis methods achieve large speedups in qualitative settings, but they depend on caching observationally equivalent candidates; this strategy breaks down for MBA because candidate outputs are quantitative bitvectors and the behavioral space is enormous. We present SIMBA (Synthesis of Mixed-Boolean Arithmetic), a GPU-accelerated MBA synthesizer built around cache-free bottom-up enumeration. SIMBA avoids language caches entirely and uses a GPU-oriented enumeration design that keeps work local and highly parallel. In experiments, SIMBA is substantially faster than prior MBA synthesis tools, handles larger specifications, and reaches expression sizes that existing methods fail to solve. These results establish cache-free GPU synthesis as a practical and scalable approach for quantitative domains, and identify it as a strong alternative to cache-centric designs.

We present GAMMA, a scalable feed-forward model that reconstructs 4D Gaussian Splatting from monocular videos, enabling dynamic novel view synthesis in real-time. Most existing approaches for dynamic scene modeling require multi-view videos as input or costly per-scene optimization. In contrast, GAMMA learns a unified 4D scene representation from monocular videos and generatively predicts 4D Gaussian primitives within seconds. The key insight of GAMMA lies in its unified Gaussian reconstruction model, which jointly estimates temporally consistent geometry, appearance, and motion from the input video. We further explore its scalability through large-scale training on a comprehensive dataset encompassing both synthetic and real-world videos. The reconstructed GAMMA representation supports interactive scene exploration via real-time rendering across timesteps and local viewpoints. Extensive experiments demonstrate that GAMMA outperforms existing methods in both reconstruction fidelity and efficiency.

Feed-forward novel view synthesis has recently shown promising results from sparse posed images, but most existing methods assume that context and target views share a fixed camera family. This homogeneous-camera assumption breaks in practical multi-sensor systems, where perspective, fisheye, and panoramic cameras may coexist and where the target projection may be unseen during training. We study feed-forward NVS across heterogeneous central cameras and identify a key ambiguity introduced by tokenization: a visual token aggregates a projection-dependent bundle of pixel rays, while existing camera encodings mainly expose absolute rays or token-center relations. To address this, we combine token-center relative Camera Positional Encodings and proposed local raymaps, a token-level representation that explicitly describes the intra-patch ray distribution summarized by each token. We further propose projection-aware 2D RoPE, which replaces raw image-grid coordinates with ray-induced angular coordinates so that relative positional reasoning is aligned across camera projections. Together, these components treat diverse cameras as calibrated samplings of a shared ray space rather than separate visual domains. On ScanNet++ with heterogeneous-camera system, our method improves over camera-conditioned baselines under mixed-camera evaluation and demonstrates zero-shot generalization to panoramic views.

Mixed-Integer Linear Programming (MILP) is a fundamental tool for combinatorial optimization with extensive real-world applications. A central challenge lies in designing efficient MILP formulations. Large Language Models (LLMs) offer new opportunities to automate the modeling process, from deriving formulations to strengthening them. To ensure correctness, we need robust methods to compare formulations. However, existing approaches evaluate formulations numerically and fail to reason about the behavior on general problem instances. We resolve this limitation by introducing a constructive notion of MILP *reformulation* that can be formalized in Lean and *machine-checked*. We develop `FLARE`, a method that uses an LLM-based agent and the Lean proof assistant to verify proposed reformulations against a reference. To evaluate our approach, we introduce *FormulationBench*, a challenging dataset of 20 problems and 116 formulations. `FLARE` outperforms existing methods with **96.9%** accuracy. For cases where formal guarantees aren't necessary, we introduce `FLARE-NL` , an LLM proxy that achieves **99.3%** accuracy. These methods enable reliable verification in automated optimization modeling.

We consider the problem of synthesizing Clifford quantum circuits for devices with all-to-all qubit connectivity. We approach this task as a reinforcement learning problem in which an agent learns to discover a sequence of elementary Clifford gates that reduces a given symplectic matrix representation of a Clifford circuit to the identity. This formulation permits a simple learning curriculum based on random walks from the identity. We introduce a novel neural network architecture that is equivariant to qubit relabelings of the symplectic matrix representation, and which is size-agnostic, allowing a single learned policy to be applied across different qubit counts without circuit splicing or network reparameterization. On six-qubit Clifford circuits, the largest regime for which optimal references are available, our agent finds circuits within one two-qubit gate of optimality in milliseconds per instance, and finds optimal circuits in 99.2\% of instances within seconds per instance. After continued training on ten-qubit instances, the agent scales to unseen Clifford tableaus with up to thirty qubits, including targets generated from circuits with over a thousand Clifford gates, where it achieves lower average two-qubit gate counts than Qiskit's Aaronson-Gottesman and greedy Clifford synthesizers.

We present SetDiff, a geometry-grounded multi-view diffusion framework that enhances novel-view renderings produced by 3D Gaussian Splatting. Our method integrates explicit 3D priors, pixel-aligned coordinate maps and pose-aware Plucker ray embeddings, into a set-based diffusion model capable of jointly processing variable numbers of reference and target views. This formulation enables robust occlusion handling, reduces hallucinations under low-signal conditions, and improves photometric fidelity in visual content restoration. A unified set mixer performs global token-level attention across all input views, supporting scalable multi-camera enhancement while maintaining computational efficiency through latent-space supervision and selective decoding. Extensive experiments on EUVS, Para-Lane, nuScenes, and DL3DV demonstrate significant gains in perceptual fidelity, structural similarity, and robustness under severe extrapolation. SetDiff establishes a state-of-the-art diffusion-based solution for realistic and reliable novel-view synthesis in autonomous driving scenarios.

LLM-based coding agents have shown strong capabilities on real-world software testing tasks, yet existing evaluations concentrate on unit test generation, leaving system testing unexplored. System testing is both practically important for software quality assurance and inherently difficult, as it demands repository-level holistic understanding and long-horizon reasoning over program behavior. In this paper, we propose to measure LLM agents' system testing capabilities through its core problem, Directed Input Synthesis (DIS): generating system-level inputs that exercise specified code branches. We introduce DIS-Bench, a benchmark built via an automated pipeline based on random testing that selects target branches that are both reachable and challenging. Because DIS-Bench is constructed from execution behavior rather than human-authored issues or patches, it is scalable and less susceptible to contamination from existing training corpora. DIS-Bench contains 8267 tasks drawn from 37 real-world repositories. We evaluate 9 mainstream LLMs on DIS-Bench-Lite under both BM25 retrieval and coding agents, including OpenHands, Mini-SWE-Agent, and Claude-Code. The results show that DIS is highly challenging: the best open-weight configuration resolves only 28.1\% of targets at pass@1, the best frontier configuration (57.8\%) still leaves over 40\% unsolved, and 23.1\% of targets are unsolved by every configuration in every attempt. We further perform a manual inspection to characterize the dominant failure modes, revealing reasoning bottlenecks of LLMs on system testing tasks.

Reflectance confocal microscopy (RCM) provides noninvasive, cellular-resolution “optical biopsies” of human skin {\em in vivo} by acquiring en-face images at successive depths, forming a sparse $z$-stack. Due to optical limitations, these stacks are anisotropic 3D volumes with lateral resolution ($0.5\mu m$) $\sim$6 times higher compared to axial resolution, which is defined by the optical sectioning ($3\mu m$), limiting the interpretation of tissue. Our goal is to provide continuous-depth visualization by interpolating intermediate sections and making the 3D volume isotropic. Such a representation permits arbitrary-direction sectioning, including histopathology-like cross-sectional examination, without requiring per-patient optimization. To that end, we introduce the first RCM-specific novel-view synthesis (NVS) approach CD-RCM: a feedforward model that predicts realistic, unseen depths from sparsely sampled RCM stacks. Classical neural rendering methods focus on reconstruction from surface-level multi-view observations. In contrast to surface-level camera views, RCM can acquire optically sectioned en-face images of tissue beyond the surface up to $200 \mu m$. However, during visualization of the RCM stacks, observations of the shallower sections (towards the surface) obscure the deeper ones. This unique axial imaging geometry and layer-dependent anatomical organization motivated our development of a tailored architectural and training framework that explicitly accounts for RCM’s depth-resolved, occlusive imaging physics. Experiments demonstrate that CD-RCM achieves high-fidelity novel-view synthesis with sub-second inference time.

Designing DNA libraries is a key challenge from drug design to protein engineering and synthetic biology. Modern generative models offer opportunities to navigate the design space and propose specific sequences predicted to be effective in-silico. Designing deterministic libraries of specific sequences is however limited by the cost of DNA synthesis -- the synthesis barrier. In contrast, high-throughput multiplexed screening can measure the function of billions of biological sequences in parallel. Harnessing this technology requires the design of randomized libraries with specific design constraints to achieve low synthesis costs. In practice, such stochastic libraries are often chosen heuristically, sacrificing control for scale. Is there a way to bridge AI-based in-silico sequence design with high-throughput experimentation? In this work, we introduce Policy Gradients for Library Design (PGLD). PGLD uses a synthesis-aware parametrization of stochastic DNA libraries and optimizes them against a specified objective function. This allows for designing massive, controlled libraries without being limited by synthesis costs. We show how PGLD enables lab-in-the-loop design of multi-round high-throughput experiments, and large-scale in-vitro DNA sampling from generative models. Finally, we use PGLD to design a library of $\sim 10^6$ unique sequences at a cost of $\sim 700$ USD to explore the mutation space of a broadly neutralizing influenza antibody.

Novel View Synthesis(NVS) for thermal infrared scenarios has become increasingly important in practical applications. Recent advances in 3D Gaussian Splatting(3DGS) have demonstrated its strong performance for NVS, and a handful of preliminary efforts have explored its extension to thermal NVS. However, existing 3DGS-based methods for thermal NVS often suffer from severe local blur in rendered images. Theoretical and empirical analyses reveal that the single-channel nature of thermal data allows intensity discrepancy to be compensated through opacity adjustment, which can erroneously amplify the opacity of floating Gaussians, especially when the background behind them is insufficiently represented. To address this issue, we propose a method termed Floating Gaussian Suppression (FGS) for thermal NVS. Specifically, we leverage an over-saturation of luminance to introduce an implicit gradient penalty, which guides the optimizer to suppress the opacity of floaters to match the ground truth. By effectively reducing the opacity of these floaters, this mechanism alleviates the occlusion of distant details, thereby mitigating local blur. Moreover, to preserve rendering fidelity, the luminance parameters of Gaussians are optimized concurrently. Experimental results demonstrate that our method effectively mitigates local blur and produces sharper and more faithful thermal reconstruction compared with the baseline methods.