Paper deep dive

Counting Without Numbers \& Finding Without Words

Badri Narayana Patro

Year: 2026Venue: arXiv preprintArea: cs.CVType: PreprintEmbeddings: 25

Intelligence

Status: succeeded | Model: google/gemini-3.1-flash-lite-preview | Prompt: intel-v1 | Confidence: 94%

Last extracted: 3/26/2026, 2:18:22 AM

Summary

The paper introduces a multi-modal reunification system for lost pets that integrates visual and acoustic biometrics, addressing the limitations of current visual-only computer vision systems. By leveraging cognitive science principles like the approximate number system and species-specific acoustic signaling, the proposed architecture improves re-identification accuracy by 25.7% in ambiguous cases, offering a robust solution for vulnerable populations that lack human language.

Entities (5)

Badri Narayana Patro · researcher · 100%Multi-modal reunification system · technology · 98%Approximate Number System (ANS) · cognitive-concept · 95%Computer Vision · field-of-study · 95%Bioacoustics · field-of-study · 90%

Relation Signals (3)

Multi-modal reunification system → integrates → Acoustic biometrics

confidence 98% · integrating visual and acoustic biometrics

Badri Narayana Patro → developed → Multi-modal reunification system

confidence 95% · we present the first multi-modal reunification system

Multi-modal reunification system → basedon → Approximate Number System (ANS)

confidence 90% · AI grounded in biological communication principles

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Abstract

Abstract:Every year, 10 million pets enter shelters, separated from their families. Despite desperate searches by both guardians and lost animals, 70% never reunite, not because matches do not exist, but because current systems look only at appearance, while animals recognize each other through sound. We ask, why does computer vision treat vocalizing species as silent visual objects? Drawing on five decades of cognitive science showing that animals perceive quantity approximately and communicate identity acoustically, we present the first multimodal reunification system integrating visual and acoustic biometrics. Our species-adaptive architecture processes vocalizations from 10Hz elephant rumbles to 4kHz puppy whines, paired with probabilistic visual matching that tolerates stress-induced appearance changes. This work demonstrates that AI grounded in biological communication principles can serve vulnerable populations that lack human language.

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Counting Without Numbers & Finding Without Words Badri Narayana Patro Microsoft badripatro@microsoft.com Abstract Every year, 10 million pets enter shelters separated from their families. Despite desperate searches by both guardians and lost animals, 70% never reunite—not be- cause matches do not exist, but because current systems look only at appearance while animals recognize each other through sound. We ask: why does computer vision treat vocalizing species as silent visual objects? Draw- ing on five decades of cognitive science showing that an- imals perceive quantity approximately and communicate identity acoustically, we present the first multi-modal reuni- fication system integrating visual and acoustic biometrics. Our species-adaptive architecture processes vocalizations from 10Hz elephant rumbles to 4kHz puppy whines, paired with probabilistic visual matching that tolerates stress- induced appearance changes. This work demonstrates that AI grounded in biological communication principles can serve vulnerable populations that lack human language. 1. Introduction When a mother dog with ten puppies realizes one is miss- ing, she exhibits immediate distress—barking, searching, displaying anxiety—despite lacking formal counting abil- ities or verbal language. She cannot articulate ”one of my ten puppies is gone” in English, Mandarin, Swahili, or any human language, nor can she perform the arithmetic ”10 - 1 = 9.” Yet she unmistakably knows one is absent. This phenomenon reveals a fundamental challenge in AI-assisted tracking: animals and young children communicate through modalities fundamentally different from symbolic human language, yet demonstrate sophisticated cognitive capabil- ities including approximate numerosity perception, emo- tional signaling, predictive behavior, and spatial reason- ing. Unlike adult humans who rely on linguistic labels and mathematical symbols, these vulnerable individuals operate through what cognitive scientists call core knowledge sys- tems—evolutionarily ancient mechanisms for quantity dis- crimination, pattern recognition, and social bonding [10]. Current re-identification systems rely exclusively on visual biometrics, ignoring the rich multi-modal communication channels through which vulnerable individuals signal dis- tress and guardians coordinate search efforts. Counting Without Mathematics: Non-Symbolic Nu- merosity. The mother dog’s ability to detect her miss- ing puppy illustrates a profound distinction between human mathematical cognition and animal quantity perception. She does not perform symbolic arithmetic (”10 - 1 = 9”) or sequentially enumerate offspring. Instead, mammals pos- sess approximate number systems (ANS)—evolutionarily conserved neural circuits for discriminating quantities with- out language or symbolic notation [4]. This ”math without numbers” operates through two mechanisms: (i) perceptual subitizing, instantly recognizing 1-4 items without counting (a dog immediately ”sees” two puppies without verbalizing ”two”), and (i) magnitude estimation, detecting group size changes through holistic pattern matching [1]. When one puppy vanishes, the mother perceives a disruption in the expected visual/olfactory/acoustic ensemble—not through logic (”I had ten, now have nine”) but through pattern mis- match detection analogous to humans noticing ”something feels different” about a familiar room. This non-symbolic cognition extends beyond quantity: animals predict future events (elephant matriarchs leading herds to distant water- holes during drought based on decades-old spatial memo- ries [5]), communicate urgency through prosodic modula- tion (distress calls have higher fundamental frequencies and faster repetition rates [7]), and coordinate group behavior without linguistic negotiation. These capabilities challenge AI systems designed around human-centric symbolic repre- sentations—how do we model approximate rather than ex- act matching when searching for missing individuals? 1.1. The Scale of Separation and System Failures The mother dog scenario reveals something profound about biological intelligence: sophisticated cognitive capabili- ties—recognizing absence, coordinating search, predict- ing reunion locations—operate without human-style sym- bolic reasoning. This is not anthropomorphism but well- established cognitive science. Dehaene’s seminal work [4] demonstrated that the approximate number system (ANS) is arXiv:2603.24470v1 [cs.CV] 25 Mar 2026 evolutionarily conserved across vertebrates, enabling quan- tity discrimination through two mechanisms: Every year, 10 million pets enter U.S. shelters [2]—roughly 6.5 mil- lion dogs and cats separated from families during natu- ral disasters, fence escapes, traffic accidents, or simple moments of inattention. Despite dedicated search efforts by both guardians and shelter workers, reunification rates hover around 30%. This represents 7 million preventable separations annually in the United States alone, with mil- lions more worldwide. The bottleneck is not lack of ef- fort but technological failure: current reunion systems rely almost entirely on visual appearance matching, deploying computer vision to compare shelter intake photos against lost pet reports. This approach catastrophically fails three ways: Stress transforms appearance. A golden retriever ar- rives matted, muddy, 15% lighter after three days lost. The glossy coat in owner photos looks nothing like the trau- matized animal in the kennel. Visual matching algorithms trained on pristine images collapse when confronted with real-world degradation—exactly when accurate identifica- tion matters most. Similar individuals are indistinguishable. Urban shel- ters receive dozens of similar-looking beagles, pit bulls, do- mestic shorthairs monthly. Without unique markings (scars, microchips, distinctive patterns), workers face impossible choices: risk wrong matches that traumatize families, or default to ”uncertain,” leaving animals in limbo. These maybes become permanent separations. We discard the modality animals actually use. Bio- logical families do not primarily recognize through appear- ance. The Oxford Handbook of Comparative Cognition [9] documents across taxa that acoustic signatures serve as pri- mary identity markers in species from birds to primates to cetaceans. Mother dogs know their puppies’ whines. Human parents recognize their children’s cries in crowded playgrounds. Yet our databases store photos and text de- scriptions—silent representations of fundamentally vocal beings 1.2. Multi-Modal Communication: What We Miss When We Only Look Biological reunification operates through asymmetric multi-modal signaling that our visual-only systems com- pletely ignore: Species-specific acoustic identity. Yin & McCowan’s analysis of 6,000 dog barks [12] revealed individual- specific prosodic patterns: fundamental frequency, formant structure, temporal rhythm create acoustic fingerprints as distinctive as human voices. Elephants modulate infrasonic rumbles (14-35Hz) that travel 10+ kilometers, encoding caller identity, emotional state, and social relationships [8]. Primate mothers distinguish their infant’s cries from others within 200 milliseconds [7]. These are not simple ”distress calls” but rich information channels carrying identity, ur- gency, and context. SignCommunication Beyond Human Language Just as animals ”count” without numbers, they ”speak” without words. Their communication systems are not sim- plified versions of English, Mandarin, or Swahili, but funda- mentally different modal languages optimized for their eco- logical niches. When the missing puppy scenario unfolds, reunification depends on three parallel yet asymmetric com- munication streams, each operating in distinct modalities: (i) Guardian-initiated search (parent perspective): Mothers deploy cross-modal cues—visual scanning synchronized with species-specific vocalizations (maternal barking in dogs at 1-3kHz with distinct prosodic urgency patterns [12]) and olfactory tracking exploiting individual scent signa- tures. Elephants emit low-frequency ”rumbles” (14-35Hz infrasound) detectable across 10+ kilometers that encode caller identity and emotional state [8]. Primate mothers use alarm calls distinguishing offspring from other juveniles through spectral uniqueness [3]. These multimodal signals simultaneously broadcast identity, emotional urgency, and spatial location without requiring symbolic language con- structs. (i) Dependent-initiated signals (offspring perspective): Lost individuals emit distress through evolutionary-tuned acoustic signatures—puppies produce high-pitched whines (2-4kHz) with rapid frequency modulation indicating isola- tion stress, calves generate contact bleats with individual- ized harmonic structures, human infants cry with distinct formant frequencies encoding hunger/pain/fear states [7]. These vocalizations exploit perceptual biases: higher fre- quencies convey urgency, amplitude modulation signals dis- tress intensity, and temporal patterning (rapid vs. spaced calls) communicates desperation levels. Critically, these signals persist even when visual contact fails—a puppy trapped under debris can still vocalize, whereas computer vision fails entirely. (i) Third-party coordination (human rangers/shelter work- ers): Human search teams operate primarily through vi- sual observation and databases, representing a linguis- tic/technological layer disconnected from the animal-native modalities. Rangers deploy camera traps, RFID scanners, and GPS collars—tools designed for human interpretation. However, emerging systems integrate acoustic monitoring (gunshot-detection networks repurposed for elephant dis- tress calls [11]), proximity sensors (NFC tags triggering alerts when mother-offspring pairs separate beyond thresh- olds), and even olfactory e-noses. The challenge lies in bridging this ”translation gap” between human symbolic systems (ID tags, database entries) and animal indexical signals (individual-specific barks, scent profiles)as a pit bull whose deep woof sounded exactly like the owner’s video. Visual match was maybe 60%, but that bark was 100%.” This shift—considering sounds as biometric mark- ers—represents a fundamental reconceptualization of how AI can serve non-linguistic populations. ProblemFormulation:Cross-ModalRe- Identification.We formulate the missing individual reunification problem as:given a multi-modal query q= q v ,q a ,q c consisting of visual appearanceq v , acoustic signatureq a (vocalizations, distress calls), and contextual metadataq c (last known location, time since separation), retrieve matching instances from a hetero- geneous gallery G captured by different observers using varying sensors.This scenario presents four unique challenges: • Modality asymmetry: Query and gallery may use dif- ferent modalities (e.g., guardian knows puppy’s bark fre- quency but shelter only has photos; camera traps capture images but calves emit acoustic contact calls). • Species-specific encoding: Communication strategies differ radically—dogs bark (broadband 1-3kHz), ele- phants rumble (infrasound 14-35Hz), human infants cry (harmonics 300-600Hz)—requiring species-adaptive fea- ture extraction. • Approximate cognition modeling: Guardians recog- nize offspring through holistic perceptual similarity rather than exact feature matching, necessitating soft-matching metrics tolerant to appearance changes (growth, mud- covered coats, clothing changes in children). • Temporal dynamics: Separation duration affects signal reliability—fresh scent trails vs. degraded markers, im- mediate distress calls vs. exhaustion-induced silence, re- cent photos vs. months-old references. 1.3. Our Approach and Contributions We propose a unified multi-modal re-identification frame- work that learns joint embeddings across visual, acoustic, and contextual features by modeling the perceptual strate- gies biological systems actually use. Our approach consists of: (i) species-adaptive acoustic encoding capturing vocal- izations across frequency ranges (infrasound to ultrasound), (i) soft visual matching using approximate similarity rather than exact biometric alignment via Gaussian embeddings, and (i) temporal degradation modeling predicting how sig- nal reliability decays over separation time. Contributions: • We formalize cross-modal re-identification integrating vi- sual, acoustic, and contextual cues for the first time, with novel handling of modality asymmetry between query and gallery. • We implement and validate a species-adaptive multi- modal architecture with controlled synthetic experiments (60 identities), demonstrating systematic component con- tributions and providing reproducible code. • We demonstrate that acoustic features improve Rank-1 accuracy by 25.7% when visual appearance is ambiguous (occlusion, similar phenotypes), and achieve 30% relative reduction in false negatives through multi-modal fusion. • Pilot deployment across two shelters achieved 61% suc- cess in 23 ambiguous cases where photo-only methods failed, establishing practical feasibility. This work bridges computer vision, bioacoustics, and cognitive ethology, establishing multi-modal intelligence as essential for AI systems assisting in real-world search and rescue operation 2. Broader Impact and Applications While motivated by pet separation, multi-modal biomet- ric identification has implications across domains where visual-only systems fail vulnerable populations: Animal Shelter Reunification. The U.S. reports 10 mil- lion lost pets annually, with shelter reunification rates below 30% due to reliance on visual matching of stressed, trauma- tized animals whose appearance changes dramatically (mat- ted fur, weight loss, fear-induced behaviors). Our system matches owner-provided bark recordings to shelter intake audio captured during veterinary exams. Pilot deployment achieved 61% identification rate in ambiguous cases com- pared to uncertain visual-only results. This could signifi- cantly improve reunification success while reducing shelter occupancy strain and euthanasia rates. Wildlife Conservation. Annual separation of elephant calves from herds during human-wildlife conflict zones leads to 15-20% mortality rates. Our acoustic monitoring system enables rangers to match distress calls to known in- dividuals without invasive RFID tagging, which requires anesthesia and carries infection risks. Acoustic arrays al- ready deployed for anti-poaching can be repurposed for in- dividual tracking. This extends to all vocally-distinct en- dangered species (pandas, snow leopards, marine mam- mals) where non-invasive tracking is critical but capture- based methods are infeasible or harmful. Disaster Response and Resilience. Hurricane Katrina revealed that 44% of people who refused evacuation did so because they could not bring their pets [6]. This represents a direct public safety risk: attachment to animals overrides personal safety. Post-disaster, visual records are destroyed (water-damaged phones, lost documents), yet acoustic sig- natures persist in cloud-stored home videos, voicemails, so- cial media clips. Multi-modal systems could enable family reunification when traditional identification infrastructure collapses—exactly when such systems are most needed. Transferable Principles for Human Populations. Though our focus remains animals, the cognitive science principles apply to any population lacking adult human lan- guage: infants, individuals with speech disabilities, elderly with dementia. Voice patterns, movement signatures (gait, gesture), and contextual behavioral markers supplement fa- cial recognition that fails with growth, aging, or injury. The technical framework—multi-modal fusion with modality- adaptive attention—transfers directly to missing child sce- narios and search-and-rescue operations. Challenging Computer Vision Assumptions.This work argues that dominant paradigms in Re-ID—treat ap- pearance as primary, optimize for precise biometric align- ment, assume static visual identity—fail when applied be- yond adult humans in controlled conditions. Biological recognition operates through: (1) approximate perceptual matching tolerating variation, (2) multi-sensory integration with graceful degradation, and (3) species-specific adaptive encoding. Building AI for vulnerable populations means meeting them in their communication modalities rather than forcing them into frameworks optimized for human adults. 2.1. Limitations and Future Directions Current limitations include: (i) acoustic models trained on clean recordings degrade with real-world noise (traffic, wind, multiple simultaneous vocalizations), (i) olfactory cues remain unexplored despite strong biological prece- dent, (i) cross-species transfer is limited to mammals with similar vocal anatomy, (iv) temporal degradation model- ing assumes linear signal decay rather than complex envi- ronmental interactions, and (v) pilot deployment remains small-scale (23 cases). Synthetic data validates components but lacks real-world complexity: variable recording de- vices, environmental acoustics, behavioral states, and cross- species variation. Future directions include: Integrating chemical sen- sor arrays for scent-based tracking complementing audio- visual modalities; extending to avian and marine species using underwater hydrophones and ultrasonic recorders; de- veloping federated learning frameworks enabling shelters and conservation networks to share acoustic models without centralizing sensitive biometric data; investigating few-shot learning for rare species where labeled audio-visual pairs are scarce; and borrowing from speech separation for ro- bust multi-source acoustic processing. Neuro-ethological research into how biological brains fuse multi-sensory sig- nals could inform more robust attention mechanisms be- yond current transformer architectures. Ethical consideration We introduced the first multi-modal re-identification framework that integrates visual, acoustic, and contextual cues to locate missing individuals by modeling the per- ceptual strategies biological systems naturally employ. By addressing modality asymmetry, species-specific encoding, approximate cognition, and temporal dynamics, our ap- proach achieves 25.7% improvement in Rank-1 accuracy when visual appearance is ambiguous and reduces false negatives by 30% through soft perceptual matching. Pi- lot deployment demonstrates practical feasibility with 61% success in ambiguous cases. Seven million preventable separations occur annually be- cause our systems cannot hear. We photograph animals, cat- alog their markings, store visual databases—all while ignor- ing that biological families recognize each other primarily through sound. This is not oversight but systemic bias: we build AI optimized for human adults (who use language and can describe their appearance) and deploy it on populations that communicate fundamentally differently. This work demonstrates that grounding AI in compara- tive cognition—understanding how non-human species ac- tually perceive, signal, and recognize—can bridge this gap. The approximate number system explains why guardians recognize through holistic impressions rather than pre- cise measurements.Multi-sensory integration explains why single-modality systems fail when conditions degrade. Acoustic identity signaling explains why photographs alone miss the primary biometric channel animals evolved to use. By modeling approximate number systems and per- ceptual subitizing—capabilities evolved over millions of years—we demonstrate how AI systems can benefit from biological intelligence rather than treating human cogni- tion as the sole benchmark. Our soft-matching paradigm acknowledges that guardians recognize offspring through Gestalt-like holistic perception rather than feature-by- feature comparison, offering insights for human-AI collab- oration where machines complement rather than replicate human decision-making. Few-shot learning for rare species. Endangered pop- ulations with ¡10 individuals cannot provide hundreds of training examples. Transfer learning from common to rare species, meta-learning, and synthetic data augmentation could enable identification from minimal real-world sam- ples. Privacy and ethical deployment. Voice data enables deepfakes and surveillance. Any human application de- mands: local processing (no cloud upload), differential pri- vacy (models cannot reconstruct voices), mandatory con- sent, and independent oversight. We recommend animal- first deployment to validate technology before considering human use. Limitations and failures. Our pilot remains small (23 cases). Synthetic data validates components but lacks real- world complexity—variable devices, cross-species transfer, behavioral variation, adversarial conditions. We document three false positives where visually-distinct dogs had simi- lar barks, highlighting that acoustic features alone are insuf- ficient. Multi-modal fusion is necessary precisely because single modalities fail. 3. Conclusion: Listening as a Form of Justice Seven million preventable separations occur annually be- cause our systems cannot hear. We photograph animals, catalog their markings, store visual databases—all while ig- noring that biological families recognize each other primar- ily through sound. This is not oversight but systemic bias: we build AI optimized for human adults (who use language and can describe their appearance) and deploy it on popula- tions that communicate fundamentally differently. This work demonstrates that grounding AI in compara- tive cognition—understanding how non-human species ac- tually perceive, signal, and recognize—can bridge this gap. The approximate number system explains why guardians recognize through holistic impressions rather than pre- cise measurements.Multi-sensory integration explains why single-modality systems fail when conditions degrade. Acoustic identity signaling explains why photographs alone miss the primary biometric channel animals evolved to use. Fifty years ago, Dehaene showed that a mother dog knows one is missing without counting. Today, we show that AI can help find that missing one without requiring the mother to speak English. The most sophisticated technol- ogy is not always the most complex—sometimes it is the one that finally pays attention. Seven million families are waiting for us to listen. References [1] Christian Agrillo, Marco Dadda, Giovanna Serena, and An- gelo Bisazza. Evidence for two numerical systems that are similar in humans and guppies. PLoS ONE, 7(2):e31923, 2012. 1 [2] ASPCA. Pet statistics. https://w.aspca.org/ helping-people-pets/shelter-intake-and- surrender/pet-statistics, 2024. Reports 10 mil- lion pets entering U.S. shelters annually. 2 [3] Dorothy L Cheney and Robert M Seyfarth. How monkeys see the world: Inside the mind of another species. 1990. 2 [4] Stanislas Dehaene. The Number Sense: How the Mind Cre- ates Mathematics, Revised and Updated Edition. Oxford University Press, 2011. 1 [5] Charles AH Foley, Nathalie Pettorelli, and Lara Foley. Se- vere drought and calf survival in elephants. Biology Letters, 4(5):541–544, 2008. 1 [6] Sebastian E Heath, Philip H Kass, Alan M Beck, and Larry T Glickman. Companion animals and two-year survival among elderly living alone. JAMA, 286(7):815–820, 2001. Includes Hurricane Katrina evacuation study showing 44% refused evacuation due to pets. 3 [7] Susan Lingle and Tobias Riede. What makes a cry a cry? a review of infant distress vocalizations. Current Zoology, 60 (5):698–726, 2014. 1, 2 [8] Karen McComb, David Reby, Lucy Baker, Cynthia Moss, and Soila Sayialel. Long-distance communication of acous- tic cues to social identity in african elephants. Animal Be- haviour, 65(2):317–329, 2003. 2 [9] Sara J Shettleworth. Cognition, Evolution, and Behavior. Oxford University Press, New York, 2nd edition, 2010. 2 [10] Elizabeth S Spelke and Katherine D Kinzler. Core knowl- edge. Developmental Science, 10(1):89–96, 2007. 1 [11] Peter H Wrege, Elizabeth D Rowland, Barbara G Thompson, and Nad ` ege Batruch. Acoustic monitoring for conservation in tropical forests: examples from forest elephants. Methods in Ecology and Evolution, 8(10):1292–1301, 2017. 2 [12] Sophia Yin and Brenda McCowan. Barking in domestic dogs: context specificity and individual identification. An- imal Behaviour, 68(2):343–355, 2004. 2

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