ANIRA: Adaptive Neural & Audio Inference

• ANIRA is a dual-use acronym representing both a recurrent Transformer framework for adaptive per-token computation and a C++ library for real-time neural network inference.
• The Transformer variant implements early allocation and online halting to align token depth with task complexity, showcasing adaptive compute control in algorithmic tasks.
• The audio variant decouples inference from the audio callback using a static thread pool, ensuring efficient real-time performance across multiple deep learning backends.

ANIRA is a name used in recent arXiv literature for two unrelated systems. In machine learning, ANIRA denotes Adaptive Neural Iterative Reasoning Architectures, a unified recurrent Transformer framework designed specifically to study and realize token-level adaptive computation with per-token variable-depth computation (Moosa et al., 9 Feb 2026). In audio engineering, anira denotes an efficient cross-platform C++ library for neural network inference in real-time audio applications, with ONNX Runtime, LibTorch, and TensorFlow Lite as backends and an execution model that decouples inference from the audio callback to a static thread pool (Ackva et al., 14 Jun 2025). This suggests that the term is currently best understood as a context-dependent acronym rather than a single standardized research object.

1. Terminological scope and disciplinary usage

The two current arXiv usages of ANIRA occupy distinct technical domains and address different classes of constraints. The recurrent-Transformer ANIRA is concerned with whether token-level compute allocation aligns with analytically controlled notions of complexity, and with whether such allocation improves algorithmic behavior (Moosa et al., 9 Feb 2026). The audio-system anira is concerned with hard real-time deployment, in which dynamic allocation, synchronization, and backend-specific runtime behavior can cause glitches or dropouts if inference is executed directly in the audio callback (Ackva et al., 14 Jun 2025).

Usage	Expansion	Domain
ANIRA	Adaptive Neural Iterative Reasoning Architectures	Token-level adaptive computation in recurrent Transformers
anira	efficient cross-platform library	Neural network inference in real-time audio applications

A plausible implication is that citation context is essential when the name appears in technical writing: in one case it denotes an experimental framework for analyzing adaptive depth in sequence models; in the other, it denotes a deployment architecture for real-time neural DSP.

2. ANIRA as Adaptive Neural Iterative Reasoning Architectures

In the machine-learning usage, ANIRA is a unified recurrent Transformer framework built to support per-token variable-depth computation while isolating compute-allocation decisions from other architectural factors (Moosa et al., 9 Feb 2026). Its backbone follows a Prelude–Recurrent–Coda structure. A prelude stack maps input tokens to contextual embeddings,

$h^{(0)}_{1:T} = P(x_{1:T}),$

after which a single Transformer block is reused up to depth $D$,

$$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$

Each token $i$ is assigned an exit depth $d_i^*$, and its exit representation is

$z_i = h_i^{(d_i^*)}.$

Once a token reaches its allocated depth, its hidden state is frozen, but it remains in attention as a fixed key–value.

The framework has two principal variants. ANIRA-E performs early allocation: immediately after the prelude, a depth decider $\psi(\cdot)$ predicts a categorical distribution over exit depths,

$q_i(d) = \psi(h_i^{(0)})_d.$

At inference, the chosen depth is $d_i^* = \arg\max_d q_i(d)$. ANIRA-O performs online halting: at recurrent step $d$, a halting decider $D$0 emits a halting probability

$D$1

which induces an exit-depth distribution through a survival process,

$D$2

At inference, ANIRA-O fixes $D$3 and selects the smallest depth whose cumulative probability reaches that threshold.

To preserve a static computation graph on GPUs, ANIRA uses a passthrough mechanism. Let $D$4 be the output of the recurrent block before freezing, and define an activity flag $D$5. The update becomes

$D$6

This ensures that inactive tokens truly stop changing while maintaining fixed-depth unrolling for execution. Compute and memory in the recurrent block scale with the mean depth

$D$7

Training combines cross-entropy on answer tokens with a compute regularizer:

$D$8

$D$9

with an exponential prior $$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$0. In the paper’s decomposition,

$$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$1

so the regularizer penalizes large expected depth while discouraging degenerate exit distributions. The decider modules are intentionally small, approximately 8% of parameters, and all recurrent steps share weights, so differences between ANIRA-E and ANIRA-O can be attributed primarily to decision timing rather than to major backbone changes.

3. Controlled evaluations, complexity alignment, and generalization limits

The recurrent-Transformer ANIRA was evaluated in a complexity-controlled evaluation paradigm using algorithmic and synthetic language tasks with parameterized difficulty (Moosa et al., 9 Feb 2026). These include MANO for modular arithmetic expressions with complexity knob given by the number of binary operators $$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$2; BREVO for graph reachability or predecessor sets with complexity tied to graph size $$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$3; CLRS-Text across 23 merged classical algorithm tasks with task-specific input size; DEPO for $$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$4-step successors on directed cycles; and LANO, a probabilistic context-free grammar benchmark with token-level parser-derived complexity proxies.

Across MANO, BREVO, CLRS-Text, and DEPO, the empirical pattern is consistent: mean depth on answer tokens increases monotonically with the relevant complexity knob. On MANO and BREVO, both ANIRA-E and ANIRA-O allocate more recurrent steps as the number of operators or graph size increases. On CLRS-Text, mean depth rises with input size across tasks. On DEPO, mean depth grows with $$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$5, consistent with the $$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$6 algorithmic cost described in the paper.

For LANO, the paper reports Spearman correlations between per-token expected depth $$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$7 and parser-derived proxies:

Proxy	ANIRA-E	ANIRA-O
Parse-space expansion	+0.666	+0.456
Parse convergence	−0.264	−0.111
# additions (parser ops)	+0.618	+0.463
# multiplications	+0.645	+0.490

These values show positive correlation with parser operation counts and parse-space expansion, and negative correlation with parse convergence. The result supports the paper’s central claim that compute allocation aligned with task complexity can emerge without explicit difficulty supervision.

The same experiments also establish the main negative result: complexity alignment does not imply algorithmic generalization. On CLRS-Text, depth continues to increase with input size even as accuracy drops sharply on unseen sizes, including sizes only slightly outside the sparse training grid. On MANO, models trained on expressions with $$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$8 perform well within that range but fall sharply for longer expressions; ANIRA-O degrades more gracefully than ANIRA-E and the non-adaptive recurrent baseline, but still poorly overall. On GSM-Symbolic, where ANIRA is retrofitted onto an 8-step recurrent Llama finetuned on Nemotron-CC-Math, neither variant allocates more depth to the more complex P1 or P2 splits than to the Main split; ANIRA-E uses mean depth on answer tokens of approximately 6 and ANIRA-O approximately 4 across all splits. The paper’s conclusion is therefore restrictive: adaptive depth can reflect complexity on the training distribution without yielding robust extrapolative algorithms.

Decision timing is another major finding. In BREVO, ANIRA-E’s depth allocation is dominated by static structural cues such as graph size and hub structure, whereas ANIRA-O’s halting behavior correlates strongly with algorithmic-state features including DFS depth, frontier size, and newly enabled nodes. In MANO, deterministic parser-derived features likewise show that ANIRA-E already allocates compute during question tokens in a way that tracks the evolving parse state, but the paper emphasizes that early decisions rely on static structural cues, whereas online halting more closely tracks algorithmic execution state. This establishes ANIRA-O as the strictly more expressive halting policy in the authors’ analysis.

4. anira as an architecture for real-time audio inference

In the audio-systems usage, anira is an efficient cross-platform library for neural network inference in real-time audio applications (Ackva et al., 14 Jun 2025). It is implemented in C++, targets Linux, macOS, and Windows, and supports ONNX Runtime 1.17.1, LibTorch 2.2.2, and TensorFlow Lite 2.16.1 as backends. Its motivating problem is that standard inference engines are optimized for average throughput rather than worst-case execution time, and can perform dynamic allocations, synchronization, and sometimes file I/O, making them unsafe to call directly from an audio callback.

The external interface centers on an InferenceHandler configured through InferenceConfig. Initialization is performed by InferenceHandler::init(bufferSize, numChannels, sampleRate) from a non-real-time context so that memory allocation and backend session setup do not occur in the callback. Optional PrePostProcessor hooks manage sequence extension, cropping, and other pre/post-processing tasks, although sample-rate conversion is explicitly outside the library’s scope.

Internally, the system comprises three principal components. InferenceManager aligns host buffers with model input sizes and calculates required latency. SessionElement maintains per-instance input/output buffers, sequence-number stamps, and thread-safe producer/consumer coordination; the paper describes both atomic-based and semaphore-based variants. ThreadPool is static and global, with high-priority worker threads and a default size of hardware_concurrency - 1. Stateless models can be parallelized across chunks, whereas stateful models cannot be parallelized across time for a single instance because state dependencies must be preserved.

The central architectural move is to decouple inference from the audio callback to a static thread pool. The callback writes samples into buffers, posts inference jobs, retrieves completed results via stamps, and optionally waits briefly for fresh output. The backend engines execute on worker threads rather than in the callback itself. The paper uses RadSan to classify real-time violations and reports that all three supported inference engines exhibit such violations. LibTorch shows the largest number, including malloc, free, calloc, mutex and rwlock operations, and for CNN-29k even sleep and fopen during initial inference; TensorFlow Lite’s violations are memory-related; ONNX Runtime exhibits malloc, free, and posix_memalign. By contrast, the paper states that ANIRA itself shows no violations in the audio callback.

Latency is modeled explicitly. The default total latency is

$$h^{(d)}_{1:T} = R\!\big(h^{(d-1)}_{1:T}\big), \quad d=1,\dots,D.$$9

where $i$0 is the host–model buffer adaptation latency, $i$1 the combined worst-case inference time in samples, $i$2 the host buffer size, and $i$3 the model’s internal latency. With an optional proportional wait time $i$4 inside the callback, the modified expression becomes

$i$5

This mechanism allows controlled latency reduction in exchange for bounded waiting via semaphore-based synchronization.

5. Benchmarking methodology and performance results in audio applications

The audio anira paper benchmarks three representative neural architectures for audio effect emulation: a CNN family inspired by Steinmetz, an RNN-2k based on a single stateful LSTM with residual connection, and an HNN-11k hybrid model combining Conv1D layers, a stateless LSTM, and a dense output layer (Ackva et al., 14 Jun 2025). The reported CNN variants are CNN-29k with 29,669 parameters, CNN-15k with 15,300 parameters, and CNN-1k with 931 parameters; the hybrid has 10,965 parameters in TensorFlow and 11,109 in LibTorch; the RNN has 1,781 parameters in TensorFlow and 1,861 in LibTorch. All models were trained on three minutes of dry/wet guitar recordings from an Ibanez TS9 Tube Screamer pedal.

Benchmarks were executed on three systems: an Intel Core i9-9980HK MacBook Pro running Arch Linux; an Apple M1 MacBook Pro running macOS 14.4.1; and an Intel Core i7-12800HX HP ZBook Fury 16 G9 running Windows 11. Buffer sizes ranged from 64 to 8192 samples at 48 kHz, and runtime was analyzed as runtime per sample (RpS) in milliseconds/sample. The benchmark harness used Google Benchmark and Google Test, repeated 50 iterations for each repetition and 10 repetitions per configuration. Statistical analysis used linear mixed-effects models with all interactions among system, engine, model, buffer size, and iteration, with random intercepts for repetition. The paper reports marginal $i$6 values of 96.0%, 95.5%, and 94.9% for its three principal model fits.

The performance conclusions are backend-specific. For stateless models, ONNX Runtime exhibits the lowest runtimes. For stateful models, LibTorch demonstrates the fastest performance. TensorFlow Lite is competitive for smaller stateless models such as CNN-1k, but is slower than LibTorch on CNN-29k and on the stateful RNN-2k. The paper also finds a strong buffer-size effect: RpS decreases as buffer size increases, with the gain per doubling largest at small block sizes and diminishing as blocks become larger.

Warm-up behavior is a distinct empirical result. For several model–engine combinations, the initial inferences take longer, and these slow initial iterations coincide with a higher incidence of real-time violations. LibTorch shows significantly higher RpS in early iterations for CNN-29k; TensorFlow Lite and ONNX Runtime also exhibit early-iteration slowdowns or fluctuations depending on model. The library therefore includes a warm_up parameter so that inference sessions can be exercised before audio processing begins.

The library’s own overhead is reported to be negligible. Using a Bypass Engine, the paper measures mean RpS values of approximately $i$7 to $i$8 ms/sample. For comparison, the real-time threshold per sample at 48 kHz is

$i$9

The paper therefore characterizes ANIRA’s own overhead as orders of magnitude below the available time per sample.

6. Comparative significance of the two ANIRA systems

The two ANIRA systems are unrelated in implementation, purpose, and evaluation protocol, but a limited methodological comparison is still possible. A plausible commonality is that both make computation scheduling explicit rather than treating it as an opaque side effect. In the recurrent-Transformer case, scheduling appears as per-token variable depth and halting policies over recurrent steps (Moosa et al., 9 Feb 2026). In the audio case, scheduling appears as decoupled inference execution, explicit latency accounting, and thread-pool orchestration under real-time deadlines (Ackva et al., 14 Jun 2025).

The differences are more fundamental than the overlap. The Transformer ANIRA is presented not as a better production model but as a research framework for understanding dynamic compute allocation. Its central claims concern complexity alignment, halting expressivity, and the distinction between structural cues and execution-state cues. The audio anira is a practical deployment architecture whose central claims concern compatibility with multiple inference engines, mitigation of real-time violations, and continuous signal flow under hard timing constraints. This suggests that “ANIRA” currently names two separate technical lineages: one interpretive and algorithmic, the other systems-oriented and deployment-driven.

Within their respective domains, both uses of ANIRA mark an effort to separate a core computational model from the policy that governs when and how computation is applied. In the Transformer setting, that separation is between recurrent computation and the decider modules. In the audio setting, it is between neural inference engines and the callback-safe orchestration layer. That structural resemblance is only partial, but it captures the main reason the same acronym can credibly appear in both literatures without referring to the same object.