Beyond Images: Integrative Multimodal Insights

• Beyond Images is a broad research initiative that transcends traditional static imaging by incorporating temporal dynamics, higher-order correlations, and contextual data.
• It leverages advanced methodologies such as optical cloning, deep anomaly detection, and multi-modal text generation to achieve superior metrics in PSNR, mAP, and retrieval accuracy.
• This approach unifies innovations in optical physics, computer vision, and multimodal reasoning to enable richer visual representations and enhanced data-centric analysis.

Searching arXiv for the cited works to ground the article in current records. {"^^^^^^^^6query6 Images\" OR ^^^^^^^^6ti:\6 cloning of arbitrary images beyond the diffraction limits\" OR ^^^^^^^^6ti:\6 humans see beyond intensity images?\" OR ^^^^^^^^6ti:\6 still images: Temporal features and input variance resilience\" OR ^^^^^^^^6ti:\6 Transformation Learning for Deep Anomaly Detection Beyond Images\"","max_results":^^^^6ti:\6query6^^^^,"sort_by":"submittedDate"} Beyond Images denotes a recurrent research move away from treating images as isolated, static, intensity-only, or purely visual objects. In the cited literature, the phrase appears in optical physics, visual neuroscience, computer vision, multimodal retrieval, anomaly detection, knowledge-graph completion, and astronomical modeling, but the underlying pattern is consistent: image-centered pipelines are replaced or extended by coherent medium responses, higher-order correlations, temporal structure, dataset-level context, learned transformations, language-mediated semantics, executable tools, or latent visual workspaces &&&^^^^6query6^^^^&&&); (&&&^^^^6ti:\6^^^^&&&); (&&&^^^^6 OR ti:\6^^^^&&&); (&&&^^^^6 OR ti:\6^^^^&&&); (&&&^^^^6 OR ti:\6^^^^&&&); (&&&^^^^6 OR ti:\6^^^^&&&); (Wang et al., 26 Nov 2025)].

^^^^6ti:\6^^^^. Meanings and scope of the term

The expression is used in several distinct senses across the literature.

Formulation	What is exceeded	Representative paper
“beyond the diffraction limits”	ordinary diffraction-limited optical transfer	(Verma et al., 2013)
“beyond intensity images”	single-point intensity sensing	(&&&^^^^6ti:\6^^^^&&&)
“beyond still images”	static-image feature extraction	(&&&^^^^6 OR ti:\6^^^^&&&)
“beyond image data” / “beyond single images”	image-specific augmentations or image-level modeling	(&&&^^^^6 OR ti:\6^^^^&&&); (&&&^^^^6ti:\6ti:\6^^^^&&&)
“beyond image captioning” / “beyond images and language”	descriptive captioning or text-only CoT	(&&&^^^^6ti:\6 OR ti:\6^^^^&&&); (Wang et al., 26 Nov 2025)

Taken together, these uses suggest that “Beyond Images” is not a single formalism. A plausible implication is that it names a family of attempts to move from direct image appearance toward richer intermediate structure: correlations, temporal context, learned priors, retrieval over datasets, executable manipulation, or text and latent representations.

^^^^6 OR ti:\6^^^^. Physical and scientific imaging beyond classical image models

In optical cloning, arbitrary transverse structure is transferred from one laser beam to another in a three-level PRESERVED_PLACEHOLDER_^^^^6query6^^^^-type PRESERVED_PLACEHOLDER_^^^^6ti:\6^^^^ system operating in a coherent population trapping configuration. The probe couples PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^, the control couples PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^, and the analysis explicitly considers the comparable-strength regime PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^, rather than the usual weak-probe limit. The medium susceptibility is spatially structured by the control profile, and the coupled paraxial Maxwell equations are solved self-consistently for both fields. Numerical propagation with Gaussian, Hermite-Gaussian, and “CPT” letter patterns shows that the probe can acquire the control image while emerging with smaller feature size. The reported reduction is about a factor of PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^ for arbitrary images and about a factor of $2.5$ for a two-peaked Hermite-Gaussian control beam; red detuning gives a fiber-like positive refractive-index channel, blue detuning produces an anti-waveguide-like profile, and at two-photon resonance the real part of susceptibility is constant so diffraction remains (Verma et al., 2013).

A different physical sense of going beyond images appears in the proposal to test whether the human visual system can perceive higher-order images rather than only intensity images. In that framework, an intensity image is based on

$$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$

whereas a high-order image depends on joint correlations such as

$$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$

The proposed experiment uses parametric down-conversion, with signal and idler sent separately to the two eyes, so that the visible pattern exists only in the joint correlations. A major obstacle is that SPDC is weak relative to human threshold, roughly $7$–PRESERVED_PLACEHOLDER_^^^^6ti:\6query6^^^^ photons, with retinal integration time PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6^^^^; the paper therefore proposes stimulated emission to amplify the correlated photon number while preserving the correlation structure. The work is explicitly a proposal: a positive result would indicate access to higher-order optical structure, while a negative result would imply perception of only blur or background (&&&^^^^6ti:\6^^^^&&&).

Astronomical imaging provides another form of “beyond” in which learned priors exceed the practical deconvolution limit without violating the underlying physics. A conditional GAN trained on PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6^^^^ Sloan Digital Sky Survey galaxy images at PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6^^^^, evaluated with PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6^^^^-fold cross validation, is used to restore images degraded by Gaussian PSFs with PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6^^^^ and noise scaled as PRESERVED_PLACEHOLDER_^^^^6ti:\66^^^^. The reported reconstruction quality is about PRESERVED_PLACEHOLDER_^^^^6ti:\6^^^^7 PSNR, compared with about PRESERVED_PLACEHOLDER_^^^^6ti:\6^^^^8 for blind deconvolution and about PRESERVED_PLACEHOLDER_^^^^6ti:\6^^^^9 for Lucy-Richardson deconvolution. The recovered structures include spiral arm structure, star-forming regions, dust lanes, overall galaxy morphology, and signs of mergers and interactions. The paper is explicit that the method does not recover truly absent information and cannot recover weak lensing shear once irrecoverable observational loss has occurred (&&&^^^^6ti:\66^^^^&&&).

Galaxy decomposition extends the same theme from restoration to structural modeling. GALFIT v^^^^6 OR ti:\6^^^^ moves beyond axisymmetric, “one ellipse per component” fitting by adding Fourier modes, bending modes, coordinate rotation functions for spirals, and truncation functions for rings, cutoffs, and dust lanes, while preserving the interpretability of the Sérsic index, effective radius, and luminosity. The generalized framework allows irregular, curved, logarithmic and power-law spirals, ring and truncated shapes to be mixed and matched across parametric components, and is motivated by quantifying asymmetry, fitting rings and spiral arms, measuring low surface brightness tidal features, and estimating model-dependent uncertainties through comparisons of plausible decompositions (&&&^^^^6ti:\6^^^^7&&&).

^^^^6 OR ti:\6^^^^. From still images to spatiotemporal media

A central modern use of the term is the move beyond static-image recognition. A brain-inspired multi-stream model trains on videos rather than still images, with a spatial stream based on a pre-trained ResNet, a temporal stream using multiple sampled frames, slow fusion, and fully connected + NetVLAD layers, and an audio stream combined through a mixture-of-experts formulation

PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6query6^^^^

The model is trained on YouTube-8M, with over PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6ti:\6^^^^ million videos and PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ classes; ImageNet is used for image pretraining and robustness tests, and HVU for video robustness tests. For the main video experiments, videos are trimmed to PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ seconds, about PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ frames; the spatial stream uses the median frame, and the temporal stream uses PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ input frames at a PRESERVED_PLACEHOLDER_^^^^6 OR ti:\66^^^^ sampling ratio. On unmodified ImageNet, ResNet reaches PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^7 and the two-stream model PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^8; on modified ImageNet, the scores are PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^9 and PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6query6^^^^. On unmodified HVU, ResNet reaches PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6ti:\6^^^^ and the two-stream model PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^; on modified HVU, the scores are PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ and PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^. The paper explicitly frames static-only training as “temporal myopia” and reports that the least decline in mAP occurred with PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ temporal frames (&&&^^^^6 OR ti:\6^^^^&&&).

Video forensics pushes the same point into synthetic-media detection. Detectors trained on synthetic images perform well on images but fail on synthetic videos, and the failure is not mainly caused by H.^^^^6 OR ti:\66 OR ti:\6^^^^ compression. The paper attributes this to trace mismatch: image generators often show periodic spectral peaks or grid-like structures associated with upsampling operations, whereas video generators leave substantially different trace patterns. When image-trained detectors are applied to videos, average video AUCs are often around PRESERVED_PLACEHOLDER_^^^^6 OR ti:\66^^^^–PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^7, and no detector consistently exceeds roughly PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^8. When the same architectures are trained directly on video data, every model reaches average patch-level AUC PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^9, with MISLnet at PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6query6^^^^, and source attribution reaches AUC PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6ti:\6^^^^ for the best model. Robust training against H.^^^^6 OR ti:\66 OR ti:\6^^^^ recompression keeps AUC above PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ for all CRFs and often PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ for PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^. Zero-shot transfer to unseen generators is difficult—reported AUCs are approximately PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ for Sora and PRESERVED_PLACEHOLDER_^^^^6 OR ti:\66^^^^ for Pika—but few-shot adaptation raises them to PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^7 and PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^8, respectively (&&&^^^^6ti:\6^^^^9&&&).

A common misconception in this area is that a video is “just a sequence of frames.” The empirical record in these papers does not support that simplification: temporal features alter robustness, and synthetic video traces differ from synthetic image traces in ways that matter operationally (&&&^^^^6 OR ti:\6^^^^&&&, &&&^^^^6ti:\6^^^^9&&&).

^^^^6 OR ti:\6^^^^. Beyond image-specific augmentations and single-image modeling

In anomaly detection, the phrase marks a move away from hand-designed image augmentations. NeuTraL AD is motivated by the observation that rotations, crops, flips, or color jitter are often meaningless for time series and tabular data. It therefore learns transformations PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^9 jointly with an encoder PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6query6^^^^, so that transformed views remain semantically close to the source while remaining distinguishable from one another. The similarity term is

PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6ti:\6^^^^

and the Deterministic Contrastive Loss becomes both the training objective and the anomaly score. The method is evaluated on time-series datasets including SAD, NATOPS, Character Trajectories, Epilepsy, and Racket Sports, and on tabular datasets including Arrhythmia, Thyroid, KDDCUP, and KDDCUP-Rev. Reported one-vs.-rest time-series AUCs include PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ on SAD, PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ on NATOPS, PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ on CT, PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6 OR ti:\6^^^^ on Epilepsy, and PRESERVED_PLACEHOLDER_^^^^6 OR ti:\66^^^^ on RS; tabular F^^^^6ti:\6^^^^ scores include PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^7 on Arrhythmia, PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^8 on Thyroid, PRESERVED_PLACEHOLDER_^^^^6 OR ti:\6^^^^9 on KDD, and $2.5$^^^^6query6^^^^ on KDDRev (&&&^^^^6 OR ti:\6^^^^&&&).

Unsupervised camouflaged object detection advances the same argument from a different direction. RISE replaces single-image feature grouping with dataset-level prototype retrieval. DINOv^^^^6 OR ti:\6^^^^ extracts a feature map $2.5$^^^^6ti:\6^^^^, spectral clustering yields a coarse foreground/background partition, cross-category retrieval selects foreground prototypes least similar to global background descriptors and vice versa, histogram-based filtering removes unreliable images, and Multi-View KNN Retrieval aggregates results across horizontal flip, vertical flip, and rotations by $2.5$^^^^6 OR ti:\6^^^^, $2.5$^^^^6 OR ti:\6^^^^, and $2.5$^^^^6 OR ti:\6^^^^. The resulting pseudo-masks are used to train SINet-V^^^^6 OR ti:\6^^^^. On CHAMELEON, CAMO, COD^^^^6ti:\6query6^^^^K, and NC^^^^6 OR ti:\6^^^^K, the best reported variant, RISE with DINOv^^^^6 OR ti:\6^^^^-ViT-L^^^^6ti:\6 OR ti:\6^^^^, reaches $2.5$^^^^6 OR ti:\6^^^^, $2.5$6, $2.5$7, $2.5$8 on CHAMELEON; $2.5$9, $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$^^^^6query6^^^^, $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$^^^^6ti:\6^^^^, $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$^^^^6 OR ti:\6^^^^ on CAMO; $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$^^^^6 OR ti:\6^^^^, $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$^^^^6 OR ti:\6^^^^, $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$^^^^6 OR ti:\6^^^^, $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$6 on COD^^^^6ti:\6query6^^^^K; and $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$7, $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$8, $$I(\mathbf r,t)=\langle E^{-}(\mathbf r,t)\,E^{+}(\mathbf r,t)\rangle,$$9, $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$^^^^6query6^^^^ on NC^^^^6 OR ti:\6^^^^K (&&&^^^^6ti:\6ti:\6^^^^&&&).

These results suggest a broader principle: when intra-image similarity is weak or image-specific augmentations are ill-defined, richer structure can be learned from transformations in latent space or from prototypes mined across the full dataset.

^^^^6 OR ti:\6^^^^. Text generation, retrieval, and memory beyond captioning

Text generation from images becomes markedly harder once captioning is no longer the target. Self-rationalization replaces descriptive output with joint generation of answers or labels and free-text explanations for VQA-X, VCR, and e-SNLI-VE. The comparative study of VLP, VA-T^^^^6 OR ti:\6^^^^, VL-T^^^^6 OR ti:\6^^^^, and VL-BART shows that CLIP features help answer prediction more consistently than explanation generation, scaling T^^^^6 OR ti:\6^^^^ does not consistently improve multimodal self-rationalization, and no single model family works universally best across tasks, datasets, and finetuning data sizes. That result is significant because it rejects the assumption that larger visually adapted LLMs automatically solve multimodal generation beyond captioning (&&&^^^^6ti:\6 OR ti:\6^^^^&&&).

A more explicitly creative version appears in image-inspired English free-verse poetry generation. The proposed system combines a deep coupled visual-poetic embedding, an RNN poem generator trained with policy gradient, a multi-modal discriminator for image-poem relevance, and a poem-style discriminator for poeticness. The work releases a human-annotated image-to-poem pair dataset with $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$^^^^6ti:\6^^^^ pairs and a public English poem corpus with $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$^^^^6 OR ti:\6^^^^ different poems in the abstract, while the table reports $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$^^^^6 OR ti:\6^^^^ poems after preprocessing. On the reported automatic evaluation, the full I^^^^6 OR ti:\6^^^^P-GAN reaches an overall score of $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$^^^^6 OR ti:\6^^^^, with relevance $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$^^^^6 OR ti:\6^^^^, BLEU-^^^^6ti:\6^^^^ $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$6, BLEU-^^^^6 OR ti:\6^^^^ $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$7, BLEU-^^^^6 OR ti:\6^^^^ $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$8, Novelty-^^^^6 OR ti:\6^^^^ $$\left\langle E^{-}(\mathbf r_1,t_1)\, E^{-}(\mathbf r_2,t_2)\cdots E^{+}(\mathbf r_2,t_2)\, E^{+}(\mathbf r_1,t_1) \right\rangle .$$9, and Novelty-^^^^6 OR ti:\6^^^^ $7$^^^^6query6^^^^. In human evaluation it scores $7$^^^^6ti:\6^^^^ for relevance, $7$^^^^6 OR ti:\6^^^^ for coherence, $7$^^^^6 OR ti:\6^^^^ for imaginativeness, and $7$^^^^6 OR ti:\6^^^^ overall (&&&^^^^6 OR ti:\6 OR ti:\6^^^^&&&).

Cross-modal retrieval also moves beyond similarity ranking. GRACE assigns each image a unique identifier string, trains an MLLM to memorize image $7$^^^^6 OR ti:\6^^^^ identifier, and then trains the same model to map text ^^^^^^^^6query6^^^^^^^^ $7$6 identifier. The framework explores string, numeric, semantic, structured, and atomic identifiers, and uses constrained beam search with a Trie of valid identifiers. The best reported identifier is atomic. On Flickr^^^^6 OR ti:\6query6^^^^K, the atomic variant reaches $7$7, $7$8, and $7$9; on MS-COCO (^^^^6 OR ti:\6^^^^K), it reaches PRESERVED_PLACEHOLDER_^^^^6ti:\6query6query6^^^^, PRESERVED_PLACEHOLDER_^^^^6ti:\6query6ti:\6^^^^, and PRESERVED_PLACEHOLDER_^^^^6ti:\6query6 OR ti:\6^^^^. The paper further reports that around PRESERVED_PLACEHOLDER_^^^^6ti:\6query6 OR ti:\6^^^^ images, GRACE becomes faster than CLIP because inference is generation from parameters rather than gallery-wide similarity computation (&&&^^^^6 OR ti:\6^^^^&&&).

6. Latent reasoning, tool use, and data-centric enrichment

Recent multimodal systems extend the same trajectory from generation to internal reasoning and external tool use. Monet trains MLLMs to reason directly within latent visual space by generating continuous embeddings that function as intermediate visual thoughts. The framework uses a three-stage distillation-based SFT pipeline, constructs Monet-SFT-^^^^6ti:\6 OR ti:\6 OR ti:\6^^^^K, and introduces VLPO because GRPO primarily enhances text-based reasoning rather than latent reasoning. A key technical device is supervision over observation-token hidden states together with latent-only backpropagation, followed by latent alignment without auxiliary images. The paper reports that Monet-7B shows consistent gains across real-world perception and reasoning benchmarks and strong out-of-distribution generalization on challenging abstract visual reasoning tasks, with the best open-source performance on VisualPuzzles (Wang et al., 26 Nov 2025).

Thyme takes a more agentic route. Instead of only conditioning on images, it allows the model to decide whether an image manipulation or a computation is needed, emit executable Python code, run it in a sandbox, inspect the returned result, and continue reasoning. The SFT stage is built from a curated dataset of roughly PRESERVED_PLACEHOLDER_^^^^6ti:\6query6 OR ti:\6^^^^K samples, derived from over PRESERVED_PLACEHOLDER_^^^^6ti:\6query6 OR ti:\6^^^^ million raw examples, and the RL stage uses PRESERVED_PLACEHOLDER_^^^^6ti:\6query66^^^^ manually collected high-resolution question-answer pairs. GRPO-ATS assigns temperature PRESERVED_PLACEHOLDER_^^^^6ti:\6query6^^^^7 to text reasoning and PRESERVED_PLACEHOLDER_^^^^6ti:\6query6^^^^8 to code generation. Reported benchmark gains include HRBench-^^^^6 OR ti:\6^^^^K from PRESERVED_PLACEHOLDER_^^^^6ti:\6query6^^^^9 to PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6query6^^^^, HRBench-8K from PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6ti:\6^^^^ to PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6 OR ti:\6^^^^, MME-RealWorld perception from PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6 OR ti:\6^^^^ to PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6 OR ti:\6^^^^, MME-RealWorld reasoning from PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6 OR ti:\6^^^^ to PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\66^^^^, V* from PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6^^^^7 to PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6^^^^8, HallusionBench from PRESERVED_PLACEHOLDER_^^^^6ti:\6ti:\6^^^^9 to PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6query6^^^^, LogicVista from PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6ti:\6^^^^ to PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6 OR ti:\6^^^^, and WeMath from PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6 OR ti:\6^^^^ to PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6 OR ti:\6^^^^ (&&&^^^^6 OR ti:\6^^^^8&&&).

A data-centric variant appears in multi-modal knowledge graphs. The Beyond Images framework enriches MMKG datasets in three stages: large-scale retrieval of additional entity-related images, conversion of all visual inputs into textual descriptions, and LLM fusion of multi-source descriptions into concise entity-aligned summaries. The enriched text replaces or augments the text modality in standard MMKG models without changing architectures or loss functions. Across MKG-W, MKG-Y, and DB^^^^6ti:\6 OR ti:\6^^^^K, and across MMRNS, MyGO, NativE, and AdaMF, the paper reports consistent gains up to PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6 OR ti:\6^^^^ Hits@^^^^6ti:\6^^^^ overall. On a manually sampled subset of PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\66^^^^ entities with logos or symbols, Baseline gives MRR PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6^^^^7 and Hits@^^^^6ti:\6^^^^ PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6^^^^8, while Fusion gives MRR PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6^^^^9 and Hits@^^^^6ti:\6^^^^ PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6query6^^^^, corresponding to PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6ti:\6^^^^ MRR and PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6 OR ti:\6^^^^ Hits@^^^^6ti:\6^^^^. The optional Text-Image Consistency Check Interface is introduced for targeted audits, and in PRESERVED_PLACEHOLDER_^^^^6ti:\6 OR ti:\6 OR ti:\6^^^^ random cases per dataset no clear mismatches were observed, with only two cases judged inaccurate or incomplete (&&&^^^^6 OR ti:\6^^^^&&&).

Across these systems, a recurring misconception is that richer multimodal behavior requires only better image encoders. The cited results point elsewhere. In some settings, the decisive step is latent supervision or policy optimization over latent embeddings; in others it is autonomous code execution; in others it is the conversion of ambiguous visuals into text. A plausible implication is that “beyond images” increasingly refers not to the abandonment of images, but to their integration into larger representational and operational pipelines (Wang et al., 26 Nov 2025, &&&^^^^6 OR ti:\6^^^^8&&&, &&&^^^^6 OR ti:\6^^^^&&&).